Publisher: Elsevier   (Total: 3206 journals)

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Showing 1 - 200 of 3206 Journals sorted alphabetically
Academic Pediatrics     Hybrid Journal   (Followers: 39, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 27, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 106, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 28, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 44, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 8)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 6)
Acta Astronautica     Hybrid Journal   (Followers: 449, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 30, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 3)
Acta de Investigación Psicológica     Open Access   (Followers: 2)
Acta Ecologica Sinica     Open Access   (Followers: 11, SJR: 0.18, CiteScore: 1)
Acta Histochemica     Hybrid Journal   (Followers: 5, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 338, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 3, SJR: 1.793, CiteScore: 6)
Acta Psychologica     Hybrid Journal   (Followers: 26, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access   (Followers: 1)
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 7, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 8)
Acute Pain     Full-text available via subscription   (Followers: 15, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 18, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 9, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 14, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 194, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 13, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 9, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 20, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 30, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 12, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 12, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 24, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 15, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 35, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 5)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 14)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 29, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 11, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 11, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 27, SJR: 1.562, CiteScore: 3)
Advances in Clinical Radiology     Full-text available via subscription   (Followers: 1)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 21, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Cosmetic Surgery     Full-text available via subscription   (Followers: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 16)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 14)
Advances in Digestive Medicine     Open Access   (Followers: 14)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 7)
Advances in Drug Research     Full-text available via subscription   (Followers: 26)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 30, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 9)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 51, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 2)
Advances in Family Practice Nursing     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 69, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 17)
Advances in Genetics     Full-text available via subscription   (Followers: 21, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 11, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 8, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 26, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 11, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 26)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 4, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 37, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 10, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 10, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 21, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 17, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 9, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 6)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 5, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 26)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Pathology     Hybrid Journal   (Followers: 1)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 5)
Advances in Oncobiology     Full-text available via subscription   (Followers: 2)
Advances in Ophthalmology and Optometry     Full-text available via subscription   (Followers: 1)
Advances in Organ Biology     Full-text available via subscription   (Followers: 2)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 18, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 6, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 27, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 19)
Advances in Pharmacology     Full-text available via subscription   (Followers: 17, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 10, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 10)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 6)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 19)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 69)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 7, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 3, SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 7)
Advances in Space Research     Full-text available via subscription   (Followers: 433, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 6)
Advances in Surgery     Full-text available via subscription   (Followers: 13, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 36, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 20)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 15)
Advances in Virus Research     Full-text available via subscription   (Followers: 6, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 57, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 398, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 12, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 485, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 18, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 32, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 47, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 4)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 58, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 8, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 12, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 12)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 2, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 11, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 11, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 56, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 6, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 6, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 5)
American Heart J.     Hybrid Journal   (Followers: 59, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 67, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 48, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 13)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 17, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 40, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 35, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 37, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 51)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics & Gynecology MFM     Hybrid Journal   (Followers: 1)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 276, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 67, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 32, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 29, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 39, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 67, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 26, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription   (Followers: 1)
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 6, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 44, SJR: 1.512, CiteScore: 5)
Analytica Chimica Acta : X     Open Access  
Analytical Biochemistry     Hybrid Journal   (Followers: 223, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 13, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 14)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 25, SJR: 0.683, CiteScore: 2)

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Similar Journals
Journal Cover
Information Systems
Journal Prestige (SJR): 0.805
Citation Impact (citeScore): 4
Number of Followers: 13  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0306-4379 - ISSN (Online) 0306-4379
Published by Elsevier Homepage  [3206 journals]
  • Using agile methodologies for adopting COBIT
    • Abstract: Publication date: Available online 19 February 2020Source: Information SystemsAuthor(s): Ana Cláudia Amorim, Miguel Mira da Silva, Rúben Pereira, Margarida GonçalvesAbstractCOBIT 5 is a widely-used framework for implementing sound governance of enterprise IT (GEIT). Currently, the ISACA’s official implementation solution follows a sequentially ordered process, raising several issues related with lack of commitment from top management and misaligned solutions. Nevertheless, new project life-cycle strategies have emerged along with the agile paradigm for project management, providing flexible and adaptable environments for projects where the solution is complex and not clear, delivering the product incrementally with feedback loops. This research aims to eliminate some known challenges of COBIT 5 adoptions by providing a Scrum based methodology to address these programmes. Design Science Research Methodology was used to guide this work, where two iterations on the solution development, demonstration and evaluation activities were performed. With two different iterations to the same solution and two distinct demonstrations, it was possible to identify some relevant findings. Overall, the results showed that an agile methodology is not sufficient by itself to reduce the resistance to change in the processes, nevertheless increased the commitment from senior managers on the adoption of new practices and enabled the detection of scope misalignment earlier when developing the solutions.
  • A-Cure: An accurate information reconstruction from inaccurate data
    • Abstract: Publication date: Available online 17 February 2020Source: Information SystemsAuthor(s): Jiawei Xu, Vladimir Zadorozhny, John GrantAbstractWe address the challenge of reconstructing historical information from aggregated, possibly inaccurate historical reports. For example, given a mixture of accurate and inaccurate monthly and weekly sums, how can we find accurately the daily counts of people infected with flu' We propose an approach, called A-Cure, that performs automatic data reconstruction from a combination of accurate and inaccurate (noisy) reports in two phases: (1) it estimates the sequence of reports in order of their inaccuracy utilizing sequential RMSE metrics (S-RMSE) that compare reconstructions from different subsets of the reports; (2) it eliminates the inaccurate reports that impact the reconstruction error and recovers the target data with high accuracy. A-Cure implements an efficient information recovery framework utilizing the advanced InCompFuse (Xu et al., 2019) incompatibility analysis method. In particular, A-Cure uses an Incompatibility Graph (IG) and Energy Flow (EF) method and quantifies the accuracy of the result by the use of a Reconstruction Quality value. We compare our method with related approaches and demonstrate that our method significantly improves on their results and yields a reconstruction of high quality. The source code of A-Cure is available on GitHub.1
  • Re-ranking via local embeddings: A use case with permutation-based
           indexing and the nSimplex projection
    • Abstract: Publication date: Available online 13 February 2020Source: Information SystemsAuthor(s): Lucia Vadicamo, Claudio Gennaro, Fabrizio Falchi, Edgar Chávez, Richard Connor, Giuseppe AmatoAbstractApproximate Nearest Neighbor (ANN) search is a prevalent paradigm for searching intrinsically high dimensional objects in large-scale data sets. Recently, the permutation-based approach for ANN has attracted a lot of interest due to its versatility in being used in the more general class of metric spaces. In this approach, the entire database is ranked by a permutation distance to the query. Typically, permutations allow the efficient selection of a candidate set of results, but typically to achieve high recall or precision this set has to be reviewed using the original metric and data. This can lead to a sizeable percentage of the database being recalled, along with many expensive distance calculations.To reduce the number of metric computations and the number of database elements accessed, we propose here a re-ranking based on a local embedding using the nSimplex projection. The nSimplex projection produces Euclidean vectors from objects in metric spaces which possess the n-point property. The mapping is obtained from the distances to a set of reference objects, and the original metric can be lower bounded and upper bounded by the Euclidean distance of objects sharing the same set of references.Our approach is particularly advantageous for extensive databases or expensive metric function. We reuse the distances computed in the permutations in the first stage, and hence the memory footprint of the index is not increased.An extensive experimental evaluation of our approach is presented, demonstrating excellent results even on a set of hundreds of millions of objects.
  • Bitpart: Exact metric search in high(er) dimensions
    • Abstract: Publication date: Available online 4 February 2020Source: Information SystemsAuthor(s): Alan Dearle, Richard ConnorAbstractWe define BitPart (Bitwise representations of binary Partitions), a novel exact search mechanism intended for use in high-dimensional spaces. In outline, a fixed set of reference objects is used to define a large set of regions within the original space, and each data item is characterised according to its containment within these regions. In contrast with other mechanisms only a subset of this information is selected, according to the query, before a search within the re-cast space is performed. Partial data representations are accessed only if they are known to be potentially useful towards the calculation of the exact query solution.Our mechanism requires Ω(NlogN) space to evaluate a query, where N is the cardinality of the data, and therefore does not scale as well as previously defined mechanisms with low-dimensional data. However it has recently been shown that, for a nearest neighbour search in high dimensions, a sequential scan of the data is essentially unavoidable. This result has been suspected for a long time, and has been referred to as the curse of dimensionality in this context.In the light of this result, the compromise achieved by this work is to make the best possible use of the available fast memory, and to offer great potential for parallel query evaluation. To our knowledge, it gives the best compromise currently known for performing exact search over data whose dimensionality is too high to allow the useful application of metric indexing, yet is still sufficiently low to give at least some traction from the metric and supermetric properties.
  • On the declarative paradigm in hybrid business process representations: A
           conceptual framework and a systematic literature study
    • Abstract: Publication date: Available online 24 January 2020Source: Information SystemsAuthor(s): Amine Abbad Andaloussi, Andrea Burattin, Tijs Slaats, Ekkart Kindler, Barbara WeberAbstractProcess modeling plays a central role in the development of today’s process-aware information systems both on the management level (e.g., providing input for requirements elicitation and fostering communication) and on the enactment level (providing a blue-print for process execution and enabling simulation). The literature comprises a variety of process modeling approaches proposing different modeling languages (i.e., imperative and declarative languages) and different types of process artifact support (i.e., process models, textual process descriptions, and guided simulations). However, the use of an individual modeling language or a single type of process artifact is usually not enough to provide a clear and concise understanding of the process. To overcome this limitation, a set of so-called “hybrid” approaches combining languages and artifacts have been proposed, but no common grounds have been set to define and categorize them. This work aims at providing a fundamental understanding of these hybrid approaches by defining a unified terminology, providing a conceptual framework and proposing an overarching overview to identify and analyze them. Since no common terminology has been used in the literature, we combined existing concepts and ontologies to define a “Hybrid Business Process Representation” (HBPR). Afterward, we conducted a Systematic Literature Review (SLR) to identify and investigate the characteristics of HBPRs combining imperative and declarative languages or artifacts. The SLR resulted in 30 articles which were analyzed. The results indicate the presence of two distinct research lines and show common motivations driving the emergence of HBPRs, a limited maturity of existing approaches, and diverse application domains. Moreover, the results are synthesized into a taxonomy classifying different types of representations. Finally, the outcome of the study is used to provide a research agenda delineating the directions for future work.
  • A novel graph-based clustering method using noise cutting
    • Abstract: Publication date: July 2020Source: Information Systems, Volume 91Author(s): Lin-Tao Li, Zhong-Yang Xiong, Qi-Zhu Dai, Yong-Fang Zha, Yu-Fang Zhang, Jing-Pei DanAbstractRecently, many methods have appeared in the field of cluster analysis. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns. Algorithms based on graphs provide good results for this problem. However, some widely used graph-based clustering methods, such as spectral clustering algorithms, are sensitive to noise and outliers. In this paper, a cut-point clustering algorithm (CutPC) based on a natural neighbor graph is proposed. The CutPC method performs noise cutting when a cut-point value is above the critical value. Normally, the method can automatically identify clusters with arbitrary shapes and detect outliers without any prior knowledge or preparatory parameter settings. The user can also adjust a coefficient to adapt clustering solutions for particular problems better. Experimental results on various synthetic and real-world datasets demonstrate the obvious superiority of CutPC compared with k-means, DBSCAN, DPC, SC, and DCore.
  • Clustering biomedical and gene expression datasets with kernel density and
           unique neighborhood set based vein detection
    • Abstract: Publication date: Available online 23 January 2020Source: Information SystemsAuthor(s): Md Anisur Rahman, Li-Minn Ang, Kah Phooi SengAbstractIt is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.
  • Discovering and merging related analytic datasets
    • Abstract: Publication date: Available online 17 January 2020Source: Information SystemsAuthor(s): Rutian Liu, Eric Simon, Bernd Amann, Stéphane GançarskiAbstractThe production of analytic datasets is a significant big data trend and has gone well beyond the scope of traditional IT-governed dataset development. Analytic datasets are now created by data scientists and data analysts using big data frameworks and agile data preparation tools. However, despite the profusion of available datasets, it remains quite difficult for a data analyst to start from a dataset at hand and customize it with additional attributes coming from other existing datasets. This article describes a model and algorithms that exploit automatically extracted and user-defined semantic relationships for extending analytic datasets with new atomic or aggregated attribute values. Our framework is implemented as a REST service in SAP HANA and includes a careful theoretical analysis and practical solutions for several complex data quality issues.
  • Understanding Service-Oriented Architecture (SOA): A systematic literature
           review and directions for further investigation
    • Abstract: Publication date: Available online 17 January 2020Source: Information SystemsAuthor(s): Naghmeh Niknejad, Waidah Ismail, Imran Ghani, Behzad Nazari, Mahadi Bahari, Ab Razak Bin Che HussinAbstractService-Oriented Architecture (SOA) has emerged as an architectural approach that enhances the service delivery performance of existing traditional systems while still retaining their most important features. This approach, due to its flexibility of adoption, has gained the attention of both academic and business entities, especially in the development of world-leading technologies such as Cloud Computing (CC) and the Internet of Things (IoT). Although many studies have listed the success factors of SOA, a few minor failures have also been reported in the literature. Despite the availability of rich material on SOA, there is a lack of systematic reviews covering the different aspects of the SOA concept in Information Systems (IS) research. Therefore, the central objective of this study is to review existing issues of SOA and share the findings with the academia. Hence, a systematic literature review (SLR) was conducted to analyse existing studies related to SOA and the factors that led to SOA success and failure from 2009 to 2019. To completely cover all SOA-related research in the IS field, a two-stage review protocol that included automatic and manual searching was applied, resulting in 103 primary studies. The articles were categorised into four research themes, namely: SOA Adoption, SOA Concepts, SOA Impact, and SOA Practice. The result shows that the academic research interest on SOA increased recently with most of the articles covering SOA Practice followed by SOA Adoption. Moreover, the findings of this review highlighted SOA Governance, SOA Strategy, Financial Issues and Costs, and Education and Training as the most significant factors of SOA adoption and implementation. Consequently, the outcomes will assist professionals and experts in organisations as well as academic researchers to focus more on these factors for successfully adopting and implementing SOA.
  • Improving malicious URLs detection via feature engineering: Linear and
           nonlinear space transformation methods
    • Abstract: Publication date: Available online 15 January 2020Source: Information SystemsAuthor(s): Tie Li, Gang Kou, Yi PengAbstractIn malicious URLs detection, traditional classifiers are challenged because the data volume is huge, patterns are changing over time, and the correlations among features are complicated. Feature engineering plays an important role in addressing these problems. To better represent the underlying problem and improve the performance of classifiers in identifying malicious URLs, this paper proposed a combination of linear and non-linear space transformation methods. For linear transformation, a two-stage distance metric learning approach was developed: first, singular value decomposition was performed to get an orthogonal space, and then a linear programming was used to solve an optimal distance metric. For nonlinear transformation, we introduced Nyström method for kernel approximation and used the revised distance metric for its radial basis function such that the merits of both linear and non-linear transformations can be utilized. 331622 URLs with 62 features were collected to validate the proposed feature engineering methods. The results showed that the proposed methods significantly improved the efficiency and performance of certain classifiers, such as k-Nearest Neighbor, Support Vector Machine, and neural networks. The malicious URLs’ identification rate of k-Nearest Neighbor was increased from 68% to 86%, the rate of linear Support Vector Machine was increased from 58% to 81%, and the rate of Multi-Layer Perceptron was increased from 63% to 82%. We also developed a website to demonstrate a malicious URLs detection system which uses the methods proposed in this paper. The system can be accessed at:
  • A comprehensive analysis of delayed insertions in metric access methods
    • Abstract: Publication date: Available online 11 January 2020Source: Information SystemsAuthor(s): Humberto Razente, Maria Camila N. Barioni, Regis M. Santos SousaAbstractSimilarity queries are fundamental operations for applications that deal with complex data. This paper presents MIA (Metric Indexing Assisted by auxiliary memory with limited capacity), a new delayed insertion approach that can be employed to create enhanced dynamic metric access methods through short-term memories. We present a comprehensive evaluation of delayed insertion methods for metric access methods while comparing MIA to dynamic forced reinsertions. Our experimental results show that metric access methods can benefit from these strategies, decreasing the node overlap, the number of distance calculations, the number of disk accesses, and the execution time to run k-nearest neighbor queries.
  • Information system ecology: An application of dataphoric ascendancy
    • Abstract: Publication date: March 2020Source: Information Systems, Volume 89Author(s): Michael J. Pritchard, J.C. MartelAbstractInformation systems, like biological systems, are susceptible to external perturbations. Similar to flora and fauna in a biome, species of data can be classified within a dataphora. While entropic properties and data geometries can be used to describe local species of data within a dataphora, they are not designed to describe the global properties of an information system or evaluate its stability. Ecologists have used Information Theories to describe macro-level properties of biological ecosystems and statistical tools to evaluate biological systems. This research leverages an ecological perspective to model information systems as a living system. Our findings support the theory of dataphoric ascendancy with Wikipedia having a Diversity Index value of 0.68, within the range of 0.65 and 0.80 that indicates a balanced state. We further support our findings with additional evaluations of other ecosystems including the predicted collapse of the information service known as the Digital Universe. This research allows for an information system’s stability to be (a) characterized and (b) predicted using ecological measures specific to the diversity of data within the ecosystem.
  • Automated discovery of declarative process models with correlated data
    • Abstract: Publication date: March 2020Source: Information Systems, Volume 89Author(s): Volodymyr Leno, Marlon Dumas, Fabrizio Maria Maggi, Marcello La Rosa, Artem PolyvyanyyAbstractAutomated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints. Declarative process discovery techniques have been shown to produce simpler models than procedural ones, particularly for processes with high variability. However, the bulk of approaches for automated discovery of declarative process models focus on the control-flow perspective, ignoring the data perspective. This paper addresses the problem of discovering declarative process models with data conditions. Specifically, the paper tackles the problem of discovering constraints that involve two activities of the process such that each of these two activities is associated with a condition that must hold when the activity occurs. The paper presents and compares two approaches to the problem of discovering such conditions. The first approach uses clustering techniques in conjunction with a rule mining technique, while the second approach relies on redescription mining techniques. The two approaches (and their variants) are empirically compared using a combination of synthetic and real-life event logs. The experimental results show that the former approach outperforms the latter when it comes to re-discovering constraints artificially injected in a log. Also, the former approach is in most of the cases more computationally efficient. On the other hand, redescription mining discovers rules with higher confidence (and lower support) suggesting that it may be used to discover constraints that hold for smaller subsets of cases of a process.
  • Discovering instance and process spanning constraints from process
           execution logs
    • Abstract: Publication date: March 2020Source: Information Systems, Volume 89Author(s): Karolin Winter, Florian Stertz, Stefanie Rinderle-MaAbstractInstance spanning constraints (ISC) are the instrument to establish controls across multiple instances of one or several processes. A multitude of applications crave for ISC support. Consider, for example, the bundling and unbundling of cargo across several instances of a logistics process or dependencies between examinations in different medical treatment processes. Non-compliance with ISC can lead to severe consequences and penalties, e.g., dangerous effects due to undesired drug interactions. ISC might stem from regulatory documents, extracted by domain experts. Another source for ISC are process execution logs. Process execution logs store execution information for process instances, and hence, inherently, the effects of ISC. Discovering ISC from process execution logs can support ISC design and implementation (if the ISC was not known beforehand) and the validation of the ISC during its life time. This work contributes a categorization of ISC as well as four discovery algorithms for ISC candidates from process execution logs. The discovered ISC candidates are put into context of the associated processes and can be further validated with domain experts. The algorithms are prototypically implemented and evaluated based on artificial and real-world process execution logs. The results facilitate ISC design as well as validation and hence contribute to a digitalized ISC and compliance management.
  • JSON: Data model and query languages
    • Abstract: Publication date: March 2020Source: Information Systems, Volume 89Author(s): Pierre Bourhis, Juan L. Reutter, Domagoj VrgočAbstractDespite the fact that JSON is currently one of the most popular formats for exchanging data on the Web, there are very few studies on this topic and there is no agreement upon a theoretical framework for dealing with JSON. Therefore in this paper we propose a formal data model for JSON documents and, based on the common features present in available systems using JSON, we define a lightweight query language allowing us to navigate through JSON documents, study the complexity of basic computational tasks associated with this language, and compare its expressive power with practical languages for managing JSON data.
  • An empirical evaluation of exact set similarity join techniques using GPUs
    • Abstract: Publication date: March 2020Source: Information Systems, Volume 89Author(s): Christos Bellas, Anastasios GounarisAbstractExact set similarity join is a notoriously expensive operation, for which several solutions have been proposed. Recently, there have been studies that present a comparative analysis using MapReduce or a non-parallel setting. Our contribution is that we complement these works through conducting a thorough evaluation of the state-of-the-art GPU-enabled techniques. These techniques are highly diverse in their key features and our experiments manage to reveal the key strengths of each one. As we explain, in real-life applications there is no dominant solution. Depending on specific dataset and query characteristics, each solution, even not using the GPU at all, has its own sweet spot. All our work is repeatable and extensible.
  • Compatible byte-addressable direct I/O for peripheral memory devices in
    • Abstract: Publication date: Available online 2 January 2020Source: Information SystemsAuthor(s): Sung Hoon Baek, Ki-Woong ParkAbstractMemory devices can be used as storage systems to provide a lower latency that can be achieved by disk and flash storage. However, traditional buffered input/output (I/O) and direct I/O are not optimized for memory-based storages. Traditional buffered I/O includes a redundant memory copy with a disk cache. Traditional direct I/O does not support byte addressing. Memory-mapped direct I/O, which optimizes file operations for byte-addressable persistent memory and appears to the CPU as a main memory. However, it has an interface that is not always compatible with existing applications. In addition, it cannot be used for peripheral memory devices (e.g., networked memory devices and hardware RAM drives) that are not interfaced with the memory bus. This paper presents a new Linux I/O layer, byte direct I/O (BDIO), that can process byte-addressable direct I/O using the standard application programming interface. It requires no modification of existing application programs and can be used not only for the memory but also for the peripheral memory devices that are not addressable by a memory management unit. The proposed BDIO layer allows file systems and device drivers to easily support BDIO. The new I/O achieved 18% to 102% performance improvements in the evaluation experiments conducted with online transaction processing, file server, and desktop virtualization storage.
  • An Alternative View on Data Processing Pipelines from the DOLAP 2019
    • Abstract: Publication date: Available online 27 December 2019Source: Information SystemsAuthor(s): Oscar Romero, Robert Wrembel, Il-Yeol SongAbstractData science requires constructing data processing pipelines (DPPs), which span diverse phases such as data integration, cleaning, pre-processing, and analysis. However, current solutions lack a strong data engineering perspective. As consequence, DPPs are error-prone, inefficient w.r.t. human efforts, and inefficient w.r.t. execution time. We claim that DPP design, development, testing, deployment, and execution should benefit from a standardized DPP architecture and from well-known data engineering solutions. This claim is supported by our experience in real projects and trends in the field, and it opens new paths for research and technology. With this spirit, we outline five research opportunities that represent novel trends towards building DPPs. Finally, we highlight that the best DOLAP 2019 papers selected for the DOLAP 2019 Information Systems Special Issue fall in this category and highlight the relevance of advanced data engineering for data science.
  • Two-stage optimization for machine learning workflow
    • Abstract: Publication date: Available online 9 December 2019Source: Information SystemsAuthor(s): Alexandre QuemyAbstractMachine learning techniques play a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners.For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as AutoML.In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.
  • Feedback driven improvement of data preparation pipelines
    • Abstract: Publication date: Available online 6 December 2019Source: Information SystemsAuthor(s): Nikolaos Konstantinou, Norman W. PatonAbstractData preparation, whether for populating enterprise data warehouses or as a precursor to more exploratory analyses, is recognised as being laborious, and as a result is a barrier to cost-effective data analysis. Several steps that recur within data preparation pipelines are amenable to automation, but it seems important that automated decisions can be refined in the light of user feedback on data products. There has been significant work on how individual data preparation steps can be refined in the light of feedback. This paper goes further, by proposing an approach in which feedback on the correctness of values in a data product can be used to revise the results of diverse data preparation components. The approach uses statistical techniques, both in determining which actions should be applied to refine the data preparation process and to identify the values on which it would be most useful to obtain further feedback. The approach has been implemented to refine the results of matching, mapping and data repair components in the VADA data preparation system, and is evaluated using deep web and open government data sets from the real estate domain. The experiments have shown how the approach enables feedback to be assimilated effectively for use with individual data preparation components, and furthermore that synergies result from applying the feedback to several data preparation components.
  • Editorial
    • Abstract: Publication date: Available online 6 December 2019Source: Information SystemsAuthor(s): John Krogstie, Hajo Reijers
  • CoPModL: Construction Process Modeling Language and Satisfiability
    • Abstract: Publication date: Available online 27 November 2019Source: Information SystemsAuthor(s): Elisa Marengo, Werner Nutt, Matthias PerktoldAbstractProcess modeling has been widely investigated in the literature and several general purpose approaches have been introduced, addressing a variety of domains. However, generality goes to the detriment of the possibility to model details and peculiarities of a particular application domain. As acknowledged by the literature, known approaches predominantly focus on one aspect between control flow and data, thus neglecting the interplay between the two. Moreover, process instances are not considered or considered in isolation, neglecting, among other aspects, synchronization points among them. As a consequence, the model is an approximation of the real process, limiting its reliability and usefulness in particular domains. This observation emerged clearly in the context of a research project in the construction domain, where preliminary attempts to model inter-company processes show the lack of an appropriate language.Building on a semi-formal language tested on real construction projects, in this paper we define CoPModL, a process modeling language which accounts both for activities and items on which activities are to be executed. The language supports the specification of different item-based dependencies among the activities, thus serving as a synchronization specification among several activity instances. We provide a formal semantics for the language in terms of LTL over finite traces. This paves the way for the development of automatic reasoning. In this respect, we investigate process model satisfiability and develop an effective algorithm to check it.
  • Special issue: BPM 2018 selected papers in foundations and engineering
    • Abstract: Publication date: Available online 26 November 2019Source: Information SystemsAuthor(s): Marco Montali, Ingo Weber, Mathias Weske, Manfred Reichert
  • Design principles for the General Data Protection Regulation (GDPR): A
           formal concept analysis and its evaluation
    • Abstract: Publication date: Available online 20 November 2019Source: Information SystemsAuthor(s): Damian A. TamburriAbstractData and software are nowadays one and the same: for this very reason, the European Union (EU) and other governments introduce frameworks for data protection — a key example being the General Data Protection Regulation (GDPR). However, GDPR compliance is not straightforward: its text is not written by software or information engineers but rather, by lawyers and policy-makers. As a design aid to information engineers aiming for GDPR compliance, as well as an aid to software users’ understanding of the regulation, this article offers a systematic synthesis and discussion of it, distilled by the mathematical analysis method known as Formal Concept Analysis (FCA). By its principles, GDPR is synthesized as a concept lattice, that is, a formal summary of the regulation, featuring 144372 records — its uses are manifold. For example, the lattice captures so-called attribute implications, the implicit logical relations across the regulation, and their intensity. These results can be used as drivers during systems and services (re-)design, development, operation, or information systems’ refactoring towards more GDPR consistency.
  • Operator implementation of Result Set Dependent KWS scoring functions
    • Abstract: Publication date: Available online 18 November 2019Source: Information SystemsAuthor(s): Vinay M.S., Jayant R. HaritsaAbstractA popular approach to hosting Keyword Search Systems (KWS) on relational DBMS platforms is to employ the Candidate Network framework. The quality of a Candidate Network-based search is critically dependent on the scoring function used to rank the relevant answers. In this paper, we first demonstrate, through detailed empirical and conceptual analysis studies, that the Labrador scoring function provides the best user relevance among contemporary Candidate Network scoring functions.Efficiently incorporating the Labrador function, however, is rendered difficult due to its Result Set Dependent (RSD) characteristic, wherein the distribution of keywords in the query results influences the ranking. To address this RSD challenge ►We investigate two mechanisms ►(a) a simple wrapper approach that leverages existing RDBMS functionalities through an SQL wrapper ►And (b) a more sophisticated operator approach wherein the database engine is augmented with custom operators that perform result ranking in the query execution plan.The above strategies have been implemented on a PostgreSQL codebase, inclusive of integration with the optimizer for the operator approach. A detailed empirical study over real-world data sets, including DBLP and Wikipedia, indicates that the wrapper approach addresses the RSD efficiency issue to a limited extent only. More encouragingly, the operator approach is extremely successful, delivering processing times that are comparable to, or better than, those of non-RSD implementations. We expect these results to aid in the organic hosting of KWS functionality on database systems.
  • To index or not to index: Time-space trade-offs for positional ranking
           functions in search engines
    • Abstract: Publication date: Available online 14 November 2019Source: Information SystemsAuthor(s): Diego Arroyuelo, Senén González, Mauricio Marin, Mauricio Oyarzún, Torsten Suel, Luis ValenzuelaAbstractPositional ranking functions, widely used in web search engines and related search systems, improve result quality by exploiting the positions of the query terms within documents. However, it is well known that positional indexes demand large amounts of extra space, typically about three times the space of a basic nonpositional index. Textual data, on the other hand, is needed to produce text snippets. In this paper, we study time-space trade-offs for search engines with positional ranking functions and text snippet generation. We consider both index-based and non-index based alternatives for positional data. We aim to answer the question of whether positional data should be indexed, and how.We show that there is a wide range of practical time-space trade-offs. Moreover, we show that using about 1.30 times the space of position al data, we can store everything needed for efficient query processing, with a minor increase in query time. This yields considerable space savings and outperforms, both in space and time, recent alternatives from literature. We also propose several efficient compressed text representations for snippet generation, which are able to use about half of the space of current state-of-the-art alternatives with little impact in query processing time.
  • Volunteering for Linked Data Wrapper maintenance: A platform perspective
    • Abstract: Publication date: Available online 8 November 2019Source: Information SystemsAuthor(s): Iker Azpeitia, Jon Iturrioz, Oscar DíazAbstractLinked Data Wrappers (LDWs) turn Web APIs into RDF end-points, leveraging the Linked Open Data cloud with current data. Unfortunately, LDWs are fragile upon upgrades on the underlying APIs, compromising LDW stability. Hence, for API-based LDWs to become a sustainable foundation for the Web of Data, we should recognize LDW maintenance as a continuous effort that outlives their breakout projects. This is not new in Software Engineering. Other projects in the past faced similar issues. The strategy: becoming open source and turning towards dedicated platforms. By making LDWs open, we permit others not only to inspect (hence, increasing trust and consumption), but also to maintain (to cope with API upgrades) and reuse (to adapt for their own purposes). Promoting consumption, adaptation and reuse might all help to increase the user base, and in so doing, might provide the critical mass of volunteers, current LDW projects lack. Drawing upon the Helping Theory, we investigate three enablers of volunteering applied to LDW maintenance: impetus to respond, positive evaluation of contributing and increasing awareness. Insights are fleshed out through SYQL, a LDW platform on top of Yahoo’s YQL. Specifically, SYQL capitalizes on the YQL community (i.e. impetus to respond), providesannotation overlays to easy participation (i.e. positive evaluation of contributing), and introduces aHealth Checker (i.e. increasing awareness). Evaluation is conducted for 12 subjects involved in maintaining someone else’s LDWs. Results indicate that both the Health Checker and the annotation overlays provide utility as enablers of awareness and contribution.
  • Comparing the expressiveness of downward fragments of the relation algebra
           with transitive closure on trees
    • Abstract: Publication date: Available online 8 November 2019Source: Information SystemsAuthor(s): Jelle Hellings, Marc Gyssens, Yuqing Wu, Dirk Van Gucht, Jan Van den Bussche, Stijn Vansummeren, George H.L. FletcherAbstractMotivated by the continuing interest in the tree data model, we study the expressive power of downward navigational query languages on trees and chains. Basic navigational queries are built from the identity relation and edge relations using composition and union. We study the effects on relative expressiveness when we add transitive closure, projections, coprojections, intersection, and difference; this for Boolean queries and path queries on labeled and unlabeled structures. In all cases, we present the complete Hasse diagram. In particular, we establish, for each query language fragment that we study on trees, whether it is closed under difference and intersection.
  • Formalising and animating multiple instances in BPMN collaborations
    • Abstract: Publication date: Available online 1 November 2019Source: Information SystemsAuthor(s): Flavio Corradini, Chiara Muzi, Barbara Re, Lorenzo Rossi, Francesco TiezziThe increasing adoption of modelling methods contributes to a better understanding of the flow of processes, from the internal behaviour of a single organisation to a wider perspective where several organisations exchange messages. In this regard, BPMN collaborations provide a suitable modelling abstraction. Even if this is a widely accepted notation, only a limited effort has been expended in formalising its semantics, especially for what it concerns the interplay among control features, data handling and exchange of messages in scenarios requiring multiple instances of interacting participants. In this paper, we face the problem of providing a formal semantics for BPMN collaborations including elements dealing with multiple instances, i.e., multi-instance pools and sequential/parallel multi-instance tasks. For an accurate account of these features, it is necessary to consider the data perspective of collaboration models, thus supporting data objects, data collections and data stores, and different execution modalities of tasks concerning atomicity and concurrency. Beyond defining a novel formalisation, we also provide a BPMN collaboration animator tool, named MIDA, faithfully implementing the formal semantics. MIDA can also support designers in debugging multi-instance collaboration models.
  • A DSL for WSN software components coordination
    • Abstract: Publication date: Available online 31 October 2019Source: Information SystemsAuthor(s): Marcos Aurélio Carrero, Martin A. Musicante, Aldri Luiz dos Santos, Carmem S. HaraAbstractWireless Sensor Networks (WSNs) have become an integral part of urban scenarios. They are usually composed of a large number of devices. Developing systems for such networks is a hard task and often involves validation on simulation environments before deployment on real settings. Component-based development allows systems to be built from reusable, existing components that share a common interface. This paper proposes a domain specific language (DSL) for coordination of WSN software components. The language provides high-level composition primitives to promote a flexible coordination execution flow and interaction between them. We present the language specification as well as a case study of an in-network WSN data storage coordination. The current specification of the language generates code for the NS2 simulation environment. The case study shows that the language implements a flexible development model. Moreover, we analyse the code reusability promoted by the language and show that it reduces the programming effort in a component-based development framework.
  • What does existing NeuroIS research focus on'
    • Abstract: Publication date: Available online 28 October 2019Source: Information SystemsAuthor(s): Jie Xiong, Meiyun ZuoAbstractNeuroIS is a research field in which neuroscience theories and tools are used to better understand information systems phenomena. At present, NeuroIS is still an emerging field in information systems, and the number of available studies is limited. Because researchers who plan or execute NeuroIS research need to understand the status of the existing empirical research published in relevant journals, we have analyzed 78 empirical articles and put forward an integrative framework for understanding what existing NeuroIS research focuses on. Our framework is built upon stimulus–organism–response theory, which explains that stimulus factors can affect users’ psychological processes, which further lead to their responses. Then, we review the collected articles and summarize their findings to give more details of NeuroIS studies. Through this literature review, we identify several opportunities for future NeuroIS research in terms of influencing factors, measurement instruments, and subjects. We believe that our work will provide some meaningful insight for future NeuroIS research.
  • Aligning observed and modelled behaviour by maximizing synchronous moves
           and using milestones
    • Abstract: Publication date: Available online 26 October 2019Source: Information SystemsAuthor(s): Vincent Bloemen, Sebastiaan van Zelst, Wil van der Aalst, Boudewijn van Dongen, Jaco van de PolAbstractGiven a process model and an event log, conformance checking aims to relate the two together, e.g. to detect discrepancies between them. For the synchronous product net of the process and a log trace, we can assign different costs to a synchronous move, and a move in the log or model. By computing a path through this (synchronous) product net, whilst minimizing the total cost, we create a so-called optimal alignment – which is considered to be the primary target result for conformance checking. Traditional alignment-based approaches (1) have performance problems for larger logs and models, and (2) do not provide reliable diagnostics for non-conforming behaviour (e.g. bottleneck analysis is based on events that did not happen). This is the reason to explore an alternative approach that maximizes the use of observed events. We also introduce the notion of milestone activities, i.e. unskippable activities, and show how the different approaches relate to each other. We propose a data structure, that can be computed from the process model, which can be used for (1) computing alignments of many log traces that maximize synchronous moves, and (2) as a means for analysing non-conforming behaviour. In our experiments we show the differences of various alignment cost functions. We also show how the performance of constructing alignments with our data structure relates to that of the state-of-the-art techniques.
  • BINet: Multi-perspective business process anomaly classification
    • Abstract: Publication date: Available online 26 October 2019Source: Information SystemsAuthor(s): Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max MühlhäuserAbstractIn this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only at a case level but also at event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets.
  • Detecting trend deviations with generic stream processing patterns
    • Abstract: Publication date: Available online 22 October 2019Source: Information SystemsAuthor(s): Massiva Roudjane, Djamal Rebaïne, Raphaël Khoury, Sylvain HalléAbstractInformation systems produce different types of event logs; in many situations, it may be desirable to look for trends inside these logs. We show how trends of various kinds can be computed over such logs in real time, using a generic framework called the trend distance workflow. Many common computations on event streams turn out to be special cases of this workflow, depending on how a handful of workflow parameters are defined. This process has been implemented and tested in a real-world event stream processing tool, called BeepBeep. Experimental results show that deviations from a reference trend can be detected in realtime for streams producing up to thousands of events per second.
  • Service contract modeling in enterprise architecture: An ontology-based
    • Abstract: Publication date: Available online 18 October 2019Source: Information SystemsAuthor(s): Cristine Griffo, João Paulo A. Almeida, Giancarlo Guizzardi, Julio Cesar NardiAbstractService contracts bind parties legally, regulating their behavior in the scope of a (business) service relationship. Given that there are legal consequences attached to service contracts, understanding the elements of a contract is key to managing services in an enterprise. After all, provisions in a service contract and in legislation establish obligations and rights for service providers and customers that must be respected in service delivery. The importance of service contracts to service provisioning in an enterprise has motivated us to investigate their representation in enterprise models. We have observed that approaches fall into two extremes of a spectrum. Some approaches, such as ArchiMate, offer an opaque “contract” construct, not revealing the rights and obligations in the scope of the governed service relationship. Other approaches, under the umbrella term “contract languages”, are devoted exactly to the formal representation of the contents of contracts. Despite the applications of contract languages, they operate at a level of detail that does not match that of enterprise architecture models. In this paper, we explore and bridge the gap between these two extremes. We address the representation of service contract elements with a systematic approach: we first propose a well-founded service contract ontology, and then extend the ArchiMate language to reflect the elements of the service contract ontology. The applicability of the proposed extension is assessed in the representation of a real-world cloud service contract.
  • Enabling runtime flexibility in data-centric and data-driven process
           execution engines
    • Abstract: Publication date: Available online 17 October 2019Source: Information SystemsAuthor(s): Kevin Andrews, Sebastian Steinau, Manfred ReichertAbstractContemporary process management systems support users during the execution of predefined business processes. However, when unforeseen situations occur, which are not part of the process model serving as the template for process execution, contemporary technology is often unable to offer adequate user support. One solution to this problem is to allow for ad-hoc changes to process models, i.e., changes that may be applied on the fly to a running process instance. As opposed to the widespread activity-centric process modeling paradigm, for which the support of instance-specific ad-hoc changes is well researched, albeit not properly supported by most commercial process engines, there is no corresponding support for ad-hoc changes in other process support paradigms, such as artifact-centric or object-aware process management. This article presents concepts for supporting ad-hoc changes in data-centric and data-driven processes, and gives insights into the challenges to be tackled when implementing this kind of process flexibility in the PHILharmonicFlows process execution engine. We evaluated the concepts by implementing a proof-of-concept prototype and applying it to various scenarios. The development of advanced flexibility features is highly relevant for data-centric processes, as the research field is generally perceived as having low maturity compared to activity-centric processes.
  • Characterizing client usage patterns and service demand for car-sharing
    • Abstract: Publication date: Available online 11 October 2019Source: Information SystemsAuthor(s): Victor A. Alencar, Felipe Rooke, Michele Cocca, Luca Vassio, Jussara Almeida, Alex Borges VieiraAbstractThe understanding of the mobility on urban spaces is useful for the creation of smarter and sustainable cities. However, getting data about urban mobility is challenging, since only a few companies have access to accurate and updated data, that is also privacy-sensitive.In this work, we characterize three distinct car-sharing systems which operate in Vancouver (Canada) and nearby regions, gathering data for more than one year. Our study uncovers patterns of users’ habits and demands for these services. We highlight the common characteristics and the main differences among car-sharing systems. Finally, we believe our study and data is useful for generating realistic synthetic workloads.
  • How meaningful are similarities in deep trajectory representations'
    • Abstract: Publication date: Available online 11 October 2019Source: Information SystemsAuthor(s): Saeed Taghizadeh, Abel Elekes, Martin Schäler, Klemens BöhmAbstractFinding similar trajectories is an important task in moving object databases. However, classical similarity models face several limitations, including scalability and robustness. Recently, an approach named t2vec proposed transforming trajectories into points in a high dimensional vector space, and this transformation approximately keeps distances between trajectories. t2vec overcomes that scalability limitation: Now it is possible to cluster millions of trajectories. However, the semantics of the learned similarity values – and whether they are meaningful – is an open issue. One can ask: How does the configuration of t2vec affect the similarity values of trajectories' Is the notion of similarity in t2vec similar, different, or even superior to existing models' As for any neural-network-based approach, inspecting the network does not help to answer these questions. So the problem we address in this paper is how to assess the meaningfulness of similarity in deep trajectory representations. Our solution is a methodology based on a set of well-defined, systematic experiments. We compare t2vec to classical models in terms of robustness and their semantics of similarity, using two real-world data sets. We give recommendations which model to use in possible application scenarios and use cases. We conclude that using t2vec in combination with classical models may be the best way to identify similar trajectories. Finally, to foster scientific advancement, we give the public access to all trained t2vec models and experiment scripts. To our knowledge, this is the biggest collection of its kind.
  • Speed prediction in large and dynamic traffic sensor networks
    • Abstract: Publication date: Available online 11 October 2019Source: Information SystemsAuthor(s): Regis Pires Magalhaes, Francesco Lettich, Jose Antonio Macedo, Franco Maria Nardini, Raffaele Perego, Chiara Renso, Roberto TraniAbstractSmart cities are nowadays equipped with pervasive networks of sensors that monitor traffic in real-time and record huge volumes of traffic data. These datasets constitute a rich source of information that can be used to extract knowledge useful for municipalities and citizens. In this paper we are interested in exploiting such data to estimate future speed in traffic sensor networks, as accurate predictions have the potential to enhance decision making capabilities of traffic management systems. Building effective speed prediction models in large cities poses important challenges that stem from the complexity of traffic patterns, the number of traffic sensors typically deployed, and the evolving nature of sensor networks. Indeed, sensors are frequently added to monitor new road segments or replaced/removed due to different reasons (e.g., maintenance). Exploiting a large number of sensors for effective speed prediction thus requires smart solutions to collect vast volumes of data and train effective prediction models. Furthermore, the dynamic nature of real-world sensor networks calls for solutions that are resilient not only to changes in traffic behavior, but also to changes in the network structure, where the cold start problem represents an important challenge. We study three different approaches in the context of large and dynamic sensor networks: local, global, and cluster-based. The local approach builds a specific prediction model for each sensor of the network. Conversely, the global approach builds a single prediction model for the whole sensor network. Finally, the cluster-based approach groups sensors into homogeneous clusters and generates a model for each cluster. We provide a large dataset, generated from ∼1.3 billion records collected by up to 272 sensors deployed in Fortaleza, Brazil, and use it to experimentally assess the effectiveness and resilience of prediction models built according to the three aforementioned approaches. The results show that the global and cluster-based approaches provide very accurate prediction models that prove to be robust to changes in traffic behavior and in the structure of sensor networks.
  • Toward higher-level abstractions based on state machine for cloud
           resources elasticity
    • Abstract: Publication date: Available online 9 October 2019Source: Information SystemsAuthor(s): Hayet Brabra, Achraf Mtibaa, Walid Gaaloul, Boualem BenatallahAbstractWith the dynamic nature of cloud applications and rapid change of their resource requirements, elasticity over cloud resources has to be effectively supported. It represents the ability to dynamically adjust cloud resources that applications use in order to adapt to their varying workloads, while maintaining the desired quality of service. However, implementing elasticity is still challenging task for cloud users as heterogeneous and low-level interfaces are provided to manage cloud resources. To alleviate this, we believe that elasticity features should be provided at resource description level. In this paper, we propose a new Cloud Resource Description Model called cRDM, which is based on State Machine formalism. Using this model, we aim at representing cloud resources while considering their elasticity behavior over the time without referring to any low level interfaces or cloud provider technical constraints. We also propose a software system based on this new specification to support the elasticity-aware orchestration of cloud resources by exploiting the underlying cloud orchestration tools and APIs. We rely on a real use case to demonstrate the applicability of the proposed system and conduct a set of experiments proving the productivity and expressiveness of the cRDM model in comparison to existing solutions. The resulted findings of our evaluation shows the efficiency of our proposal.
  • Detection and removal of infrequent behavior from event streams of
           business processes
    • Abstract: Publication date: Available online 9 October 2019Source: Information SystemsAuthor(s): Sebastiaan J. van Zelst, Mohammadreza Fani Sani, Alireza Ostovar, Raffaele Conforti, Marcello La RosaAbstractProcess mining aims at gaining insights into business processes by analyzing the event data that is generated and recorded during process execution. The vast majority of existing process mining techniques works offline, i.e. using static, historical data, stored in event logs. Recently, the notion of online process mining has emerged, in which techniques are applied on live event streams, i.e. as the process executions unfold. Analyzing event streams allows us to gain instant insights into business processes. However, most online process mining techniques assume the input stream to be completely free of noise and other anomalous behavior. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out infrequent behavior from live event streams. Our experiments show that we are able to effectively filter out events from the input stream and, as such, improve online process mining results.
  • Shared Ledger Accounting - Implementing the Economic Exchange pattern
    • Abstract: Publication date: Available online 18 September 2019Source: Information SystemsAuthor(s): Hans Weigand, Ivars Blums, Joost de KruijffAbstractDistributed Ledger Technology (DLT) suggests a new way to implement Accounting Information Systems, but an ontologically sound consensus-based design is missing to date. Against this research gap, the paper introduces a DLT-based shared ledger solution in a formal way and compliant with Financial Reporting Standards. We build on the COFRIS accounting ontology (grounded on UFO) and the blockchain ontology developed by De Kruijff & Weigand that distinguishes between a Datalogical level, an Infological and an Essential (conceptual) level. It is shown how both consensual and enterprise-specific parts of the business exchange transaction can be represented in a concise way, and how this pattern can be implemented using Smart Contracts. It is argued that the proposed Shared Ledger Accounting system increases the quality of the contents from an accounting perspective as well as the quality of the system in terms of auditability and interoperability.
  • Mining association rules for anomaly detection in dynamic process runtime
           behavior and explaining the root cause to users
    • Abstract: Publication date: Available online 18 September 2019Source: Information SystemsAuthor(s): Kristof Böhmer, Stefanie Rinderle-MaAbstractDetecting anomalies in process runtime behavior is crucial: they might reflect, on the one side, security breaches and fraudulent behaviour and on the other side desired deviations due to, for example, exceptional conditions. Both scenarios yield valuable insights for process analysts and owners, but happen due to different reasons and require a different treatment. Hence a distinction into malign and benign anomalies is required. Existing anomaly detection approaches typically fall short in supporting experts when in need to take this decision. An additional problem are false positives which could result in selecting incorrect countermeasures. This paper proposes a novel anomaly detection approach based on association rule mining. It fosters the explanation of anomalies and the estimation of their severity. In addition, the approach is able to deal with process change and flexible executions which potentially lead to false positives. This facilitates to take the appropriate countermeasure for a malign anomaly and to avoid the possible termination of benign process executions. The feasibility and result quality of the approach are shown by a prototypical implementation and by analyzing real life logs with injected artificial anomalies. The explanatory power of the presented approach is evaluated through a controlled experiment with users.
  • A deep view-point language and framework for projective modeling
    • Abstract: Publication date: Available online 18 September 2019Source: Information SystemsAuthor(s): Colin Atkinson, Christian TunjicAbstractMost view-based modeling approaches are today based on a “synthetic” approach in which the views hold all the information modeled about a system and are kept consistent using explicit, inter-view correspondence rules. The alternative “projective” approach, in which the contents of views are “projected” from a single underlying model on demand, is far less widely used due to the lack of suitable conceptual frameworks and languages. In this paper we take a step towards addressing this problem by presenting the foundations of a suitable language and conceptual framework for defining and applying views for projective modeling. The framework leverages deep modeling in order to seamlessly support views that exist at, and span, multiple levels of classification. The viewpoint language was developed in the context of Orthographic Software Modeling but is more generally applicable to any projective modeling approach.
  • Formal foundations for responsible application integration
    • Abstract: Publication date: Available online 18 September 2019Source: Information SystemsAuthor(s): Daniel Ritter, Stefanie Rinderle-Ma, Marco Montali, Andrey RivkinAbstractEnterprise Application Integration (EAI) constitutes the cornerstone in enterprise IT landscapes that are characterized by heterogeneity and distribution. Starting from established Enterprise Integration Patterns (EIPs) such as Content-based Router and Aggregator, EIP compositions are built to describe, implement, and execute integration scenarios. The EIPs and their compositions must be correct at design and runtime in order to avoid functional errors or incomplete functionalities. However, current EAI system vendors use many of the EIPs as part of their proprietary integration scenario modeling languages that are not grounded on any formalism. This renders correctness guarantees for EIPs and their composition impossible. Thus this work advocates responsible EAI based on the formalization, implementation, and correctness of EIPs. For this, requirements on an EIP formalization are collected and based on these requirements an extension of db-net, i.e., timed db-net , is proposed, fully equipped with execution semantics. It is shown how EIPs can be realized based on timed db-nets and how the correctness of these realizations can be shown. Moreover, the simulation of EIP realizations based on timed db-nets is enabled which is essential for later implementation. The concepts are evaluated in many ways, including a proof-of-concept implementation and case studies. The EIP formalization based on timed db-nets constitutes the first step towards responsible EAI.
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