157 research outputs found
Message from the ICDE 2015 Program Committee and general chairs
Since its inception in 1984, the IEEE International Conference on Data Engineering (ICDE) has become a premier forum for the exchange and dissemination of data management research results among researchers, users, practitioners, and developers. Continuing this long-standing tradition, the 31st ICDE will be hosted this year in Seoul, South Korea, from April 13 to April 17, 2015. It is our great pleasure to welcome you to ICDE 2015 and to present its proceedings to you
Algorithmes passant aÌ lâeÌchelle pour la gestion de donneÌes du Web seÌmantique sur les platformes cloud
In order to build smart systems, where machines are able to reason exactly like humans, data with semantics is a major requirement. This need led to the advent of the Semantic Web, proposing standard ways for representing and querying data with semantics. RDF is the prevalent data model used to describe web resources, and SPARQL is the query language that allows expressing queries over RDF data. Being able to store and query data with semantics triggered the development of many RDF data management systems. The rapid evolution of the Semantic Web provoked the shift from centralized data management systems to distributed ones. The first systems to appear relied on P2P and client-server architectures, while recently the focus moved to cloud computing.Cloud computing environments have strongly impacted research and development in distributed software platforms. Cloud providers offer distributed, shared-nothing infrastructures that may be used for data storage and processing. The main features of cloud computing involve scalability, fault-tolerance, and elastic allocation of computing and storage resources following the needs of the users.This thesis investigates the design and implementation of scalable algorithms and systems for cloud-based Semantic Web data management. In particular, we study the performance and cost of exploiting commercial cloud infrastructures to build Semantic Web data repositories, and the optimization of SPARQL queries for massively parallel frameworks.First, we introduce the basic concepts around Semantic Web and the main components and frameworks interacting in massively parallel cloud-based systems. In addition, we provide an extended overview of existing RDF data management systems in the centralized and distributed settings, emphasizing on the critical concepts of storage, indexing, query optimization, and infrastructure. Second, we present AMADA, an architecture for RDF data management using public cloud infrastructures. We follow the Software as a Service (SaaS) model, where the complete platform is running in the cloud and appropriate APIs are provided to the end-users for storing and retrieving RDF data. We explore various storage and querying strategies revealing pros and cons with respect to performance and also to monetary cost, which is a important new dimension to consider in public cloud services. Finally, we present CliqueSquare, a distributed RDF data management system built on top of Hadoop, incorporating a novel optimization algorithm that is able to produce massively parallel plans for SPARQL queries. We present a family of optimization algorithms, relying on n-ary (star) equality joins to build flat plans, and compare their ability to find the flattest possibles. Inspired by existing partitioning and indexing techniques we present a generic storage strategy suitable for storing RDF data in HDFS (Hadoopâs Distributed File System). Our experimental results validate the efficiency and effectiveness of the optimization algorithm demonstrating also the overall performance of the system.Afin de construire des systĂšmes intelligents, ouÌ les machines sont capables de raisonner exactement comme les humains, les donnĂ©es avec sĂ©mantique sont une exigence majeure. Ce besoin a conduit aÌ lâapparition du Web sĂ©mantique, qui propose des technologies standards pour reprĂ©senter et interroger les donnĂ©es avec sĂ©mantique. RDF est le modĂšle rĂ©pandu destineÌ aÌ dĂ©crire de façon formelle les ressources Web, et SPARQL est le langage de requĂȘte qui permet de rechercher, dâajouter, de modifier ou de supprimer des donnĂ©es RDF. Ătre capable de stocker et de rechercher des donnĂ©es avec sĂ©mantique a engendreÌ le dĂ©veloppement des nombreux systĂšmes de gestion des donnĂ©es RDF.LâĂ©volution rapide du Web sĂ©mantique a provoqueÌ le passage de systĂšmes de gestion des donnĂ©es centralisĂ©es aÌ ceux distribuĂ©s. Les premiers systĂšmes Ă©taient fondĂ©s sur les architectures pair-aÌ-pair et client-serveur, alors que rĂ©cemment lâattention se porte sur le cloud computing.Les environnements de cloud computing ont fortement impacteÌ la recherche et dĂ©veloppement dans les systĂšmes distribuĂ©s. Les fournisseurs de cloud offrent des infrastructures distribuĂ©es autonomes pouvant ĂȘtre utilisĂ©es pour le stockage et le traitement des donnĂ©es. Les principales caractĂ©ristiques du cloud computing impliquent lâĂ©volutivitĂ©Ì, la tolĂ©rance aux pannes et lâallocation Ă©lastique des ressources informatiques et de stockage en fonction des besoins des utilisateurs.Cette thĂšse Ă©tudie la conception et la mise en Ćuvre dâalgorithmes et de systĂšmes passant aÌ lâĂ©chelle pour la gestion des donnĂ©es du Web sĂ©mantique sur des platformes cloud. Plus particuliĂšrement, nous Ă©tudions la performance et le coĂ»t dâexploitation des services de cloud computing pour construire des entrepĂŽts de donneÌes du Web sĂ©mantique, ainsi que lâoptimisation de requĂȘtes SPARQL pour les cadres massivement parallĂšles.Tout dâabord, nous introduisons les concepts de base concernant le Web seÌmantique et les principaux composants des systeÌmes fondeÌs sur le cloud. En outre, nous preÌsentons un aperçu des systeÌmes de gestion des donneÌes RDF (centraliseÌs et distribueÌs), en mettant lâaccent sur les concepts critiques de stockage, dâindexation, dâoptimisation des requeÌtes et dâinfrastructure.Ensuite, nous preÌsentons AMADA, une architecture de gestion de donneÌes RDF utilisant les infrastructures de cloud public. Nous adoptons le modeÌle de logiciel en tant que service (software as a service - SaaS), ouÌ la plateforme reÌside dans le cloud et des APIs approprieÌes sont mises aÌ disposition des utilisateurs, afin quâils soient capables de stocker et de reÌcupeÌrer des donneÌes RDF. Nous explorons diverses strateÌgies de stockage et dâinterrogation, et nous eÌtudions leurs avantages et inconveÌnients au regard de la performance et du couÌt moneÌtaire, qui est une nouvelle dimension importante aÌ consideÌrer dans les services de cloud public.Enfin, nous preÌsentons CliqueSquare, un systeÌme distribueÌ de gestion des donneÌes RDF baseÌ sur Hadoop. CliqueSquare inteÌgre un nouvel algorithme dâoptimisation qui est capable de produire des plans massivement paralleÌles pour des requeÌtes SPARQL. Nous preÌsentons une famille dâalgorithmes dâoptimisation, sâappuyant sur les eÌquijointures n- aires pour geÌneÌrer des plans plats, et nous comparons leur capaciteÌ aÌ trouver les plans les plus plats possibles. InspireÌs par des techniques de partitionnement et dâindexation existantes, nous preÌsentons une strateÌgie de stockage geÌneÌrique approprieÌe au stockage de donneÌes RDF dans HDFS (Hadoop Distributed File System). Nos reÌsultats expeÌrimentaux valident lâeffectiviteÌ et lâefficaciteÌ de lâalgorithme dâoptimisation deÌmontrant eÌgalement la performance globale du systeÌme
Enhancing In-Memory Spatial Indexing with Learned Search
Spatial data is ubiquitous. Massive amounts of data are generated every day from a plethora of sources such as billions of GPS-enableddevices (e.g., cell phones, cars, and sensors), consumer-based applications (e.g., Uber and Strava), and social media platforms (e.g.,location-tagged posts on Facebook, Twitter, and Instagram). This exponential growth in spatial data has led the research communityto build systems and applications for efficient spatial data processing.In this study, we apply a recently developed machine-learned search technique for single-dimensional sorted data to spatial indexing.Specifically, we partition spatial data using six traditional spatial partitioning techniques and employ machine-learned search withineach partition to support point, range, distance, and spatial join queries. Adhering to the latest research trends, we tune the partitioningtechniques to be instance-optimized. By tuning each partitioning technique for optimal performance, we demonstrate that: (i) grid-basedindex structures outperform tree-based index structures (from 1.23Ă to 2.47Ă), (ii) learning-enhanced variants of commonly used spatialindex structures outperform their original counterparts (from 1.44Ă to 53.34Ă faster), (iii) machine-learned search within a partitionis faster than binary search by 11.79% - 39.51% when filtering on one dimension, (iv) the benefit of machine-learned search diminishesin the presence of other compute-intensive operations (e.g. scan costs in higher selectivity queries, Haversine distance computation, andpoint-in-polygon tests), and (v) index lookup is the bottleneck for tree-based structures, which could potentially be reduced by linearizingthe indexed partitions.Additional Key Words and Phrases: spatial data, indexing, machine-learning, spatial queries, geospatia
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