The Experts below are selected from a list of 4587 Experts worldwide ranked by ideXlab platform
Andrew Rau-chaplin - One of the best experts on this subject based on the ideXlab platform.
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DaWaK - Dynamic view selection for OLAP
Data Warehousing and Knowledge Discovery, 2006Co-Authors: Michael Lawrence, Andrew Rau-chaplinAbstract:Due to the increasing size of data warehouses it is often infeasible to materialize all possible aggregate views for Online Analytical Processing. View selection, the task of selecting a subset of views to materialize based on knowledge of the incoming queries and updates, is an important and challenging problem. In this paper we explore Dynamic View Selection in which the distribution of queries changes over time, and a subset of a materialized view set is updated to better serve the incoming queries.
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Dynamic view selection for OLAP
Lecture Notes in Computer Science, 2006Co-Authors: Michael Lawrence, Andrew Rau-chaplinAbstract:Due to the increasing size of data warehouses it is often infeasible to materialize all possible aggregate views for Online Analytical Processing. View selection, the task of selecting a subset of views to materialize based on knowledge of the incoming queries and updates, is an important and challenging problem. In this paper we explore Dynamic View Selection in which the distribution of queries changes over time. and a subset of a materialized view set is updated to better serve the incoming queries.
Michael Lawrence - One of the best experts on this subject based on the ideXlab platform.
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DaWaK - Dynamic view selection for OLAP
Data Warehousing and Knowledge Discovery, 2006Co-Authors: Michael Lawrence, Andrew Rau-chaplinAbstract:Due to the increasing size of data warehouses it is often infeasible to materialize all possible aggregate views for Online Analytical Processing. View selection, the task of selecting a subset of views to materialize based on knowledge of the incoming queries and updates, is an important and challenging problem. In this paper we explore Dynamic View Selection in which the distribution of queries changes over time, and a subset of a materialized view set is updated to better serve the incoming queries.
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Dynamic view selection for OLAP
Lecture Notes in Computer Science, 2006Co-Authors: Michael Lawrence, Andrew Rau-chaplinAbstract:Due to the increasing size of data warehouses it is often infeasible to materialize all possible aggregate views for Online Analytical Processing. View selection, the task of selecting a subset of views to materialize based on knowledge of the incoming queries and updates, is an important and challenging problem. In this paper we explore Dynamic View Selection in which the distribution of queries changes over time. and a subset of a materialized view set is updated to better serve the incoming queries.
Philothra Clarissa Raina - One of the best experts on this subject based on the ideXlab platform.
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kelola kubikal data transaksional sistem informasi rumah sakit dengan teknik Online Analytical Processing
Mind, 2018Co-Authors: Feri Sulianta, Philothra Clarissa RainaAbstract:Data transaksional rumah sakit dapat diberdayakan lebih lanjut untuk ragam keperluan dan bukan hanya sebagai arsip riwayat pasien perseorangan saja. Berbagai informasi berharga dapat diungkapkan dari data transkasional rumah sakit yang dihasilkan dari sistem rekam medis.Dalam kasus ini untuk mendapatkan kejelasan yang melibatkan informasi menyeluruh yang juga melibatkan ragam sudut pandang dapat disolusikan dengan teknik Online Analytical Processing (OLAP). Teknik ini mampu mengakomodasi kelengkapan data yang nantinya menjadi framework untuk dianalisa secara seksama Mengacu pada data rekam medis dimana setiap pasien memiliki banyak keluhan dan latar belakang yang berbeda yang terelasi dengan sang pasien. Teknik OLAP mampu menyajikan data dalam bentuk multidimensi. Selanjutnya, OLAP akan melakukan eksekusi data yakni slicing(irisan) dan dicing(rotasi) yakni meringkas dan mengumpulkan sejumlah besar data, melakukan filtering, pengurutan, dan memberikan peringkat (rangking) yang akan memperkaya temuan berharga dari data kubikal.
Philip S Yu - One of the best experts on this subject based on the ideXlab platform.
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EDBT - Scalable OLAP and mining of information networks
Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology - EDBT '09, 2009Co-Authors: Philip S YuAbstract:With the ubiquity of information networks and their broad applications, there have been numerous studies on the construction, Online Analytical Processing, and mining of information networks in multiple disciplines, including social network analysis, World-Wide Web, database systems, data mining, machine learning, and networked communication and information systems. In this tutorial, we present an organized picture on scalable OLAP (Online Analytical Processing) and mining of information networks, with the inclusion of the following topics: (1) an introduction to information networks and information network analysis, (2) general statistical behavior of information networks, (3) mining frequent subgraphs in large graphs and networks, (4) data integration, data cleaning and data validation in information networks, (5) clustering graphs and information networks, (6) classification of graphs and information networks; (7) summarization and simplification of graphs and information networks, (8) OLAP and multidimensional analysis of information networks, (9) evolution of dynamic information networks, and (10) research challenges on OLAP and mining of information networks.
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LOCUST: An Online Analytical Processing Framework for High Dimensional Classification of Data Streams
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Charu C Aggarwal, Philip S YuAbstract:In recent years, data streams have become ubiquitous because of advances in hardware and software technology. The ability to adapt conventional mining problems to data streams is a great challenge in a data stream environment. Many data streams are inherently high dimensional, which creates a special challenge for data mining algorithms. In this paper, we consider the problem of classification of high dimensional data streams. For the high dimensional case, even traditional classifiers do not work very well on fixed data sets. We discuss a number of insights for the intractability of the high dimensional case. We use these insights to propose a new classification method (LOCUST) which avoids many of these weaknesses. The key is to develop a subspace-based instance centered classification approach which can be implemented efficiently for a fast data stream. We propose a methodology to effectively process the data stream in an organized way, so that the intermediate data structures can be used to sample locally discriminative subspaces for the classification process. We show that LOCUST is able to work effectively in the high dimensional case, and is also flexible in terms of increased robustness with greater resource availability.
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Graph OLAP: Towards Online Analytical Processing on graphs
Proceedings - IEEE International Conference on Data Mining, ICDM, 2008Co-Authors: Chen Chen, Feida Zhu, Jia Wei Han, Xifeng Yan, Philip S YuAbstract:OLAP (On-Line Analytical Processing) is an important notion in data analysis. Recently, more and more graph or networked data sources come into being. There exists a similar need to deploy graph analysis from different perspectives and with multiple granularities. However, traditional OLAP technology cannot handle such demands because it does not consider the links among individual data tuples. In this paper, we develop a novel graph OLAP framework, which presents a multi-dimensional and multi-level view over graphs. The contributions of this work are two-fold. First, starting from basic definitions, i.e., what are dimensions and measures in the graph OLAP scenario, we develop a conceptual framework for data cubes on graphs. We also look into different semantics of OLAP operations, and classify the framework into two major subcases: informational OLAP and topological OLAP. Then, with more emphasis on informational OLAP (topological OLAP will be covered in a future study due to the lack of space), we show how a graph cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. We can see that the aggregated graphs, which depend on the graph properties of underlying networks, are much harder to compute than their traditional OLAP counterparts, due to the increased structural complexity of data. Empirical studies show insightful results on real datasets and demonstrate the efficiency of our proposed optimizations.
S. Bimonte - One of the best experts on this subject based on the ideXlab platform.
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Conceptual design and implementation of spatial data warehouses integrating regular grids of points
International Journal of Digital Earth, 2017Co-Authors: S. Bimonte, M. Zaamoune, P. BeauneAbstract:Spatial Online Analytical Processing (OLAP) and spatial data warehouse (SDW) systems are geo-business intelligence technologies that enable the analysis of huge volumes of geographic data. In the last decade, the conceptual design and implementation of SDWs that integrate spatial data, which are represented using the vector model, have been extensively investigated. However, the integration of field data (a continuous representation of spatial data) in SDWs is a recent unresolved research issue. Enhancing SDWs with field data improves the spatio-multidimensional analysis capabilities with continuity and multiresolutions. Motivated by the need for a conceptual design tool and relational Online Analytical Processing (ROLAP) implementation, we propose a UML profile for SDWs that integrates a regular grid of points and supports continuity and multiresolutions. We also propose an efficient implementation of a ROLAP architecture.
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Spatial Online Analytical Processing of geographic data through the Google Earth Interface
2011Co-Authors: M. Bertolotto, S. Bimonte, S. Di Martino, F. Ferrucci, V. Leano, B. Murgante, G. Borruso, A. LapucciAbstract:Online Analytical Processing (OLAP) tools act as support systems for decision makers to discover new knowledge hidden within data warehouses. In the spatial domain this capability is crucial. However, notwithstanding the pressing need for Spatial OLAP (SOLAP) tools, only very few are currently available. Such tools present several limitations in terms of their flexibility in the functionality and the Analytical properties they provide. To overcome these limitations, we have developed a web-based SOLAP tool, which relies on the integration of a standard Geobrowser (Google Earth) with a freely available OLAP engine, namely Mondrian. Our system allows a decision maker to perform exploration and analysis of spatial data both through the Geobrowser and a Pivot Table in a seamlessly fashion. In this paper, we illustrate the main features of the system we have developed, together with the underlying architecture, using a simulated case study.
