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Publisher: Elsevier   (Total: 3183 journals)

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Showing 1 - 200 of 3183 Journals sorted alphabetically
Academic Pediatrics     Hybrid Journal   (Followers: 38, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 26, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 102, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 28, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 40, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 7)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 6)
Acta Astronautica     Hybrid Journal   (Followers: 436, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 28, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 3)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
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: 312, 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: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
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  
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: 11, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 23)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 183, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 12, 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: 17, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 29, 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: 34, 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: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 26, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 20, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 13)
Advances in Digestive Medicine     Open Access   (Followers: 12)
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: 43, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 29, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 8)
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: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 65, 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: 10, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 7, 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: 25)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 3, 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: 9, 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: 14, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 8, 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: 24)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
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 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: 5, 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: 9, 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: 67)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 1, 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: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 421, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
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: 37, 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: 53, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 384, 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: 475, 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: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 44, 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: 10, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 53, 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: 58, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 63, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 46, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 12)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 37, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 29, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 36, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 50)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 255, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 66, 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: 28, 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: 66, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 24, 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: 5, 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: 209, 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)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 223, SJR: 1.58, CiteScore: 3)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 6, SJR: 0.937, CiteScore: 2)
Animal Reproduction Science     Hybrid Journal   (Followers: 7, SJR: 0.704, 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
Published by Elsevier Homepage  [3183 journals]
  • 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 Vieira The 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 Böhm Finding 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.
  • Bitmap filter: Speeding up exact set similarity joins with bitwise
    • Abstract: Publication date: Available online 11 October 2019Source: Information SystemsAuthor(s): Edans F.O. Sandes, George L.M. Teodoro, Alba C.M.A. Melo The Exact Set Similarity Join problem aims to find all similar sets between two collections of sets, with respect to a threshold and a similarity function such as Overlap, Jaccard, Dice or Cosine. The naïve approach verifies all pairs of sets and it is often considered impractical due the high number of combinations. So, Exact Set Similarity Join algorithms are usually based on the Filter-Verification Framework, that applies a series of filters to reduce the number of verified pairs. This paper presents a new filtering technique called Bitmap Filter, which is able to accelerate state-of-the-art algorithms for the exact Set Similarity Join problem. The Bitmap Filter uses hash functions to create bitmaps of fixed b bits, representing characteristics of the sets. Then, it applies bitwise operations (such as xor and population count) on the bitmaps in order to infer a similarity upper bound for each pair of sets. If the upper bound is below a given similarity threshold, the pair of sets is pruned. The Bitmap Filter benefits from the fact that bitwise operations are efficiently implemented by many modern general-purpose processors and it was easily applied to four state-of-the-art algorithms implemented in CPU: AllPairs, PPJoin, AdaptJoin and GroupJoin. Furthermore, we propose a Graphic Processor Unit (GPU) algorithm based on the naïve approach but using the Bitmap Filter to speedup the computation. The experiments considered 12 collections containing from 100 thousands up to 10 million sets and the joins were made using Jaccard thresholds from 0.50 to 0.95. The Bitmap Filter was able to improve 85% of the experiments in CPU, with speedups of up to 4.50× and 1.35× on average. Using the GPU algorithm, the experiments were able to speedup the original CPU algorithms by up to 577× using an Nvidia Geforce GTX 980 Ti.
  • 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 Trani Smart 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.
  • Network–wide complex event processing over geographically
           distributed data sources
    • Abstract: Publication date: Available online 10 October 2019Source: Information SystemsAuthor(s): Ioannis Flouris, Nikos Giatrakos, Antonios Deligiannakis, Minos Garofalakis In this paper we focus on Complex Event Processing (CEP) applications where the data is generated by sites that are geographically dispersed across large regions. This geographic distribution, combined with the size of the collected data, imposes severe communication and computation challenges. To attack these challenges, we propose a novel approach for geographically distributed CEP, which combines algorithmic and systems contributions. At an algorithmic level, our work combines an in-network processing approach, which pushes parts of the processing (i.e., CEP operators) towards the sources of their input events, along with a push-pull paradigm, in order to reduce the amount of communicated events. We present optimal (but computationally expensive) solutions which seek to minimize the maximum bandwidth consumption given input latency constraints for detecting events, as well as efficient greedy and heuristic algorithmic variations for our problem. At a systems level, we explain how existing CEP engines can support, with minimal modifications, our algorithms. Our experimental evaluation, using mainly real data sets and network topologies, demonstrates that the power of our techniques lies in the combination of the in-network with the push-pull paradigm, thus allowing our algorithms to significantly outperform related centralized push-pull or conventional in-network processing approaches.
  • 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 Benatallah With 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 Rosa Process 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.
  • Efficient path routing over road networks in the presence of Ad-hoc
    • Abstract: Publication date: Available online 8 October 2019Source: Information SystemsAuthor(s): Ahmed Al-Baghdadi, Xiang Lian, En Cheng Nowadays, the path routing over road networks has become increasingly important, yet challenging, in many real-world applications such as location-based services (LBS), logistics and supply chain management, transportation systems, map utilities, and so on. While many prior works aimed to find a path between a source and a destination with the smallest traveling distance/time, they do not take into account the quality constraints (e.g., obstacles) of the returned paths, such as uneven roads, roads under construction, and weather conditions on roads. Inspired by this, in this paper, we consider two types of ad-hoc obstacles, keyword-based and weather-based obstacles, on road networks, which can be used for modeling roads that the returned paths should not pass through. In the presence of such ad-hoc obstacles on roads, we formulate a path routing query over road networks with ad-hoc obstacles (PRAO), which retrieves paths from source to destination on road networks that do not pass ad-hoc keyword and weather obstacles and have the smallest traveling time. In order to efficiently answer PRAO queries, we design effective pruning methods and indexing mechanism to facilitate efficient PRAO query answering. Extensive experiments have demonstrated the efficiency and effectiveness of our approaches over real/synthetic data sets.
  • On optimal preference diffusion over social networks
    • Abstract: Publication date: Available online 3 October 2019Source: Information SystemsAuthor(s): Cheng Long, Anhua Chen, Pakawadee Pengcharoen, Raymond Chi-Wing Wong It was well observed that a user’s preference over a product changes based on his/her friends’ preferences, and this phenomenon is called “preference diffusion”, and several models have been proposed for modelling the preference diffusion process. These models share an idea that the diffusion process involves many iterations, and in each iteration, each user has his/her preference affected by some other preferences (e.g., those of his/her friends). When computing users’ preferences after a certain number of iterations, these models use users’ preferences at the end of that iteration only, which we believe is not desirable since users’ preferences at the end of other iterations should also have some effects on users’ final preferences. Therefore, in this paper, we propose a new model for preference diffusion, which takes into consideration users’ preferences at each iteration for computing users’ final preferences. Under the new model, we study two problems for optimizing the preference diffusion process with respect to two different objectives. One is easy to solve for which we design an exact algorithm and the other is NP-hard for which we design a (1−1∕e)-factor approximate algorithm. We conducted extensive experiments on real datasets which verified our proposed model and algorithms.
  • The negative skycube
    • Abstract: Publication date: Available online 27 September 2019Source: Information SystemsAuthor(s): Karim Alami, Nicolas Hanusse, Patrick Kamnang-Wanko, Sofian Maabout Multidimensional data analysis has attracted a lot of research efforts during past years. One of the aspects that has been addressed so far is that to allow users to analyze their data from different perspectives, each of which corresponds to a selected subset of dimensions. To optimize these analysis queries, precomputation, and materialization, are among most studied solutions. In the context of skyline analysis, the skycube structure has been proposed as an optimization structure to allow users to ask for the non dominated records with respect to every selected dimensions set. More precisely, given a set of dimensions D={D1,…,Dd} and a relation T(id,D), the Skycube of T is the set of all skylines obtained by considering each of the subsets of D (subspaces). To make the Skycube practically useful, two lines of research have been pursued so far: the first one aims to propose efficient algorithms for computing it. Note that the number of these skylines is exponential w.r.t. D . Hence, both execution time and storage space make these solutions struggling with even moderately large datasets, say D larger than 10 and the number of tuples greater than 106 . This motivated the second line of researches which propose Skycube summarization techniques to reduce both time and space consumption. Both lines of research, store the whole or a summary of the following information: “for every tuple t, keep track of the dimensions subsets X (subspaces) where t belongs to the respective skyline”. In this paper, we consider the complementary statement, i.e., “for every tuple t, we store a compact data structure encoding the subspaces X with respect to which, t is dominated”. This is what we call the negative skycube. Despite the apparent equivalence between the two statements (dominated vs not dominated), our analysis and extensive experiments show that these two points of view do not lead to the same behavior of the related algorithms. More specifically, our proposal shows that: (i) the negative summary can be obtained much faster than state of the art techniques for positive summaries, (ii) in general, it consumes less memory space, (iii) skyline queries evaluation using this summary is much faster, (iv) the positive Skycube can be obtained more rapidly than state of the art algorithms especially designed for this purpose, and (v) it is highly effective with respect to insertions and deletions.
  • 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 Kruijff Distributed 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-Ma Detecting 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 Tunjic Most 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 Rivkin Enterprise 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.
  • Secure lightweight password authenticated key exchange for heterogeneous
           wireless sensor networks
    • Abstract: Publication date: Available online 7 September 2019Source: Information SystemsAuthor(s): Iván Santos-González, Alexandra Rivero-García, Mike Burmester, Jorge Munilla, Pino Caballero-Gil Several three-party password authenticated key exchange (3-PAKE) protocols have recently been proposed for heterogeneous wireless sensor networks (HWSN). These are efficient and designed to address security concerns in ad-hoc sensor network applications for a global Internet of Things framework, where a user may request access to sensitive information collected by resource-constrained sensors in clusters managed by gateway nodes. In this paper we first analyze three recently proposed 3-PAKE protocols and discuss their vulnerabilities. Then, based on Radio Frequency Identification technologies we propose a novel 3-PAKE protocol for HWSN applications, with two extensions for additional security features, that is provably secure, efficient and flexible.
  • A survey of modeling language specification techniques
    • Abstract: Publication date: Available online 12 August 2019Source: Information SystemsAuthor(s): Dominik Bork, Dimitris Karagiannis, Benedikt Pittl Visual modeling languages such as the Business Process Model and Notation and the Unified Modeling Language are widely used in industry and academia for the analysis and design of information systems. Such modeling languages are usually introduced in overarching specifications which are maintained by standardization institutions such as the Object Management Group or the Open Group. Being the primary - often the single - source of information, such specifications are of paramount importance for modelers, researchers, and tool vendors. However, structure, content, and specification techniques of such documents have never been systematically analyzed. This paper addresses this gap by reporting on a Systematic Literature Review aimed to analyze published standard modeling language specifications. In total, eleven specifications were found and comprehensively analyzed. The survey reveals heterogeneity in: (i) the modeling language concepts being specified, and (ii) the techniques being employed for the specification of these concepts. The identified specification techniques are analyzed and presented by referring to their utilization in the specifications. This survey provides a foundation for research aiming to increase consistency and improve comprehensiveness of information systems modeling languages.
  • Dual-PISA: An index for aggregation operations on time series data
    • Abstract: Publication date: Available online 7 August 2019Source: Information SystemsAuthor(s): Jialin Qiao, Xiangdong Huang, Jianmin Wang, Raymond K. Wong Aggregation operations play an essential role in time series database management. As the number of data increases, it’s difficult for current solutions, such as summary table and MapReduce-based methods to respond to such queries with low latency. Other approaches, such as segment tree-based methods, have a poor insertion performance when the data size exceeds the available memory. This paper proposes a Persistent Index for Segmented Aggregations (PISA), which has fast insertion performance and low latency for aggregation queries. PISA uses a forest to overcome the performance disadvantage of insertion in traditional segment trees. By defining two kinds of tags, namely code number and serial number, we propose an algorithm to accelerate queries by avoiding unnecessary reading data on disk. Additionally, we extend it to Dual-PISA to tolerate a range of unordered data, which is very important in the real world. Dual-PISA is stored on disk and is hugely memory-efficient - only takes a few hundred bytes of memory for billions of data points. Dual-PISA can be easily implemented on both traditional databases and NoSQL systems. It handles aggregation queries within milliseconds on a commodity server, for a time range that contains tens of billions of data points.
  • A significance-based trust-aware recommendation approach
    • Abstract: Publication date: Available online 31 July 2019Source: Information SystemsAuthor(s): Faezeh Sadat Gohari, Fereidoon Shams Aliee, Hassan Haghighi Trust-aware recommender systems have been widely used in recent years to improve the performance of traditional collaborative filtering systems. A common assumption of existing trust models is that all items have the same importance for all users. However, it is reasonable to expect that some items are more significant than others in making recommendations. Furthermore, the significance of an item is not the same for all users but varies depending on many factors such as the demographic characteristics of users. For example, an item that is important to women may not be important to men. Also, the significance of an item for an individual user is not static and can change throughout the life cycle (from childhood to old age). Thus, items that are currently important to a user may become less important in the future. In this paper, we propose a Significance-Based Trust-Aware Recommendation (SBTAR) approach, which uses a new trust measure based on the concept of item significance. The significance of an item for a user is measured with respect to the demographic context of the user. Thus, SBTAR can adapt to dynamic changes in user preferences. To model demographic context, SBTAR uses Shuffled Frog Leaping Algorithm (SFLA), which is a meta-heuristic optimization technique based on the social behavior of frogs. SFLA has the advantages of simplicity, fast convergence, strong global search ability and easy implementation. Experimental results show that the proposed approach is more effective and efficient than several state-of-the-art recommendation approaches.
  • k +spatial+join+querying+processing+algorithm+based+on+spark&rft.title=Information+Systems&rft.issn=0306-4379&">A top- k spatial join querying processing algorithm based on spark
    • Abstract: Publication date: Available online 22 July 2019Source: Information SystemsAuthor(s): Baiyou Qiao, Bing Hu, Junhai Zhu, Gang Wu, Christophe Giraud-Carrier, Guoren Wang Aiming at the problem of top-k spatial join query processing in cloud computing systems, a Spark-based top-k spatial join (STKSJ) query processing algorithm is proposed. In this algorithm, the whole data space is divided into grid cells of the same size by a grid partitioning method, and each spatial object in one data set is projected into a grid cell. The Minimum Bounding Rectangle (MBR) of all spatial objects in each grid cell is computed. The spatial objects overlapping with these MBRs in another spatial data set are replicated to the corresponding grid cells, thereby filtering out spatial objects for which there are no join results, thus reducing the cost of subsequent spatial join processing. An improved plane sweeping algorithm is also proposed that speeds up the scanning mode and applies threshold filtering, thus greatly reducing the communication and computation costs of intermediate join results in subsequent top-k aggregation operations. Experimental results on synthetic and real data sets show that the proposed algorithm has clear advantages, and better performance than existing top-k spatial join query processing algorithms.
  • Customized query auto-completion and suggestion — A review
    • Abstract: Publication date: January 2020Source: Information Systems, Volume 87Author(s): Saedeh Tahery, Saeed Farzi Nowadays, with the widespread use of the internet, users meet their information needs with the help of search engines. Users tend to retrieve the most relevant results by entering short phrases in the search engines. Customizing the retrieved results helps attain this goal. In this study, research works in the fields of query suggestion, particularly query auto-completion have been studied with special attention to customization. First, the sophisticated customizing features were classified into four dimensions: time, location, context, and demographic features. Then, related works were investigated regarding algorithm, dataset and evaluation measures. Regarding the literature, we found that the research works employing context or time as sophisticated features for customization are more than those using location or demographic features. While the location dimension has been recently taken into consideration, using other dimensions has a long background. Moreover, in the related works, the AOL dataset and Mean Reciprocal Rank (MRR) are known as the most frequent dataset and evaluation measure, respectively.
  • Temporal data exchange
    • Abstract: Publication date: Available online 11 July 2019Source: Information SystemsAuthor(s): Ladan Golshanara, Jan Chomicki Data exchange is the problem of transforming data that is structured under a source schema into data structured under another schema, called the target schema, so that both the source and target data satisfy the relationship between the schemas. Many applications such as planning, scheduling, medical and fraud detection systems, require data exchange in the context of temporal data. Even though the formal framework of data exchange for relational database systems is well-established, it does not immediately carry over to the settings of temporal data, which necessitates reasoning over unbounded periods of time.In this work, we study data exchange for temporal data. We first motivate the need for two views of temporal data: the concrete view, which depicts how temporal data is compactly represented and on which the implementations are based, and the abstract view, which defines the semantics of temporal data as a sequence of snapshots. We first extend the chase procedure for the abstract view to have a conceptual basis for the data exchange for temporal databases. Considering non-temporal source-to-target tuple generating dependencies and equality generating dependencies, the chase algorithm can be applied on each snapshot independently. Then we define a chase procedure (called c-chase) on concrete instances and show the result of c-chase on a concrete instance is semantically aligned with the result of chase on the corresponding abstract instance. In order to interpret intervals as constants while checking if a dependency or a query is satisfied by a concrete database, we will normalize the instance with respect to the dependency or the query. To obtain the semantic alignment, the nulls (which are introduced by data exchange and model incompleteness) in the concrete view are annotated with temporal information. Furthermore, we show that the result of the concrete chase provides a foundation for query answering. We define naïve evaluation on the result of the c-chase and show it produces certain answers.
  • Quality-aware skill translation models for expert finding on StackOverflow
    • Abstract: Publication date: Available online 11 July 2019Source: Information SystemsAuthor(s): Arash Dargahi Nobari, Mahmood Neshati, Sajad Sotudeh Gharebagh StackOverflow has become an emerging resource for talent recognition in recent years. While users exploit technical language on StackOverflow, recruiters try to find the relevant candidates for jobs using their own terminology. This procedure implies a gap which exists between recruiters and candidates terms. Due to this gap, the state-of-the-art expert finding models cannot effectively address the expert finding problem on StackOverflow. We propose two translation models to bridge this gap. The first approach is a statistical method and the second is based on word embedding approach. Utilizing several translations for a given query during the scoring step, the result of each intermediate query is blended together to obtain the final ranking. Here, we propose a new approach which takes the quality of documents into account in scoring step. We have made several observations to visualize the effectiveness of the translation approaches and also the quality-aware scoring approach. Our experiments indicate the following: First, while statistical and word embedding translation approaches provide different translations for each query, both can considerably improve the recall. Besides, the quality-aware scoring approach can improve the precision remarkably. Finally, our best proposed method can improve the MAP measure up to 46% on average, in comparison with the state-of-the-art expert finding approach.
  • Understanding and improving ontology reasoning efficiency through learning
           and ranking
    • Abstract: Publication date: Available online 10 July 2019Source: Information SystemsAuthor(s): Yong-Bin Kang, Shonali Krishnaswamy, Wudhichart Sawangphol, Lianli Gao, Yuan-Fang Li Ontologies are the fundamental building blocks of the Semantic Web and Linked Data. Reasoning is critical to ensure the logical consistency of ontologies, and to compute inferred knowledge from an ontology. It has been shown both theoretically and empirically that, despite decades of intensive work on optimising ontology reasoning algorithms, performing core reasoning tasks on large and expressive ontologies is time-consuming and resource-intensive. In this paper, we present the meta-reasoning framework R2O2* to tackle the important problems of understanding the source of TBox reasoning hardness and predicting and optimising TBox reasoning efficiency by exploiting machine learning techniques. R2O2* combines state-of-the-art OWL 2 DL reasoners as well as an efficient OWL 2 EL reasoner as components, and predicts the most efficient one by using an ensemble of robust learning algorithms including XGBoost and Random Forests. A comprehensive evaluation on a large and carefully curated ontology corpus shows that R2O2* outperforms all six component reasoners as well as AutoFolio, a robust and strong algorithm selection system.
  • Pivot-based approximate k-NN similarity joins for big high-dimensional
    • Abstract: Publication date: Available online 2 July 2019Source: Information SystemsAuthor(s): Přemysl Čech, Jakub Lokoč, Yasin N. Silva Given an appropriate similarity model, the k-nearest neighbor similarity join represents a useful yet costly operator for data mining, data analysis and data exploration applications. The time to evaluate the operator depends on the size of datasets, data distribution and the dimensionality of data representations. For vast volumes of high-dimensional data, only distributed and approximate approaches make the joins practically feasible. In this paper, we investigate and evaluate the performance of multiple MapReduce-based approximate k-NN similarity join approaches on two leading Big Data systems Apache Hadoop and Spark. Focusing on the metric space approach relying on reference dataset objects (pivots), this paper investigates distributed similarity join techniques with and without approximation guarantees and also proposes high-dimensional extensions to previously proposed algorithms. The paper describes the design guidelines, algorithmic details, and key theoretical underpinnings of the compared approaches and also presents the empirical performance evaluation, approximation precision, and scalability properties of the implemented algorithms. Moreover, the Spark source code of all these algorithms has been made publicly available. Key findings of the experimental analysis are that randomly initialized pivot-based methods perform well with big high-dimensional data and that, in general, the selection of the best algorithm depends on the desired levels of approximation guarantee, precision and execution time.
  • An exploration of IoT platform development
    • Abstract: Publication date: Available online 28 June 2019Source: Information SystemsAuthor(s): Mahdi Fahmideh, Didar Zowghi IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens’ quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed.
  • Self-indexed motion planning
    • Abstract: Publication date: Available online 15 May 2019Source: Information SystemsAuthor(s): Angello Hoyos, Ubaldo Ruiz, Edgar Chavez, Eric S. Tellez Motion planning is a central problem for robotics. A practical way to address it is building a graph-based representation (a roadmap) capturing the connectivity of the configuration space. The Probabilistic Road Map (PRM) is perhaps the most widely used method by the robotics community based on that idea. A key sub-problem for discovering and maintaining a collision-free path in the PRM is inserting new sample points and connecting them with the k-nearest neighbors in the previous set. Instead of following the usual solution of indexing the points and then building the PRM with successive k-NN queries, we propose an approximation of the k-Nearest Neighbors Graph using the PRM as a self-index. The motivation for this construction comes from the Approximate Proximity Graph (APG), which is an index for searching proximal objects in a metric space. Using this approach the estimation of the k-NN is improved while simultaneously reducing the total time and space needed to compute a PRM. We present simulations for high-dimensional configuration spaces with and without obstacles, showing significant improvement over the standard techniques used by the robotics community.
  • ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor
    • Abstract: Publication date: Available online 21 February 2019Source: Information SystemsAuthor(s): Martin Aumüller, Erik Bernhardsson, Alexander Faithfull This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several different ways of integrating k-NN algorithms, and its configuration system automatically tests a range of parameter settings for each algorithm. Algorithms are compared with respect to many different (approximate) quality measures, and adding more is easy and fast; the included plotting front-ends can visualise these as images, LaTeX plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of k-NN algorithms. In the short term, this overview allows users to choose the correct k-NN algorithm and parameters for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different approaches to k-NN search yield comparable quality-performance trade-offs. The system is available at
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