The Experts below are selected from a list of 76314 Experts worldwide ranked by ideXlab platform
Ladjel Bellatreche - One of the best experts on this subject based on the ideXlab platform.
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eco physic eco physical design initiative for very large databases
Information Systems, 2017Co-Authors: Amine Roukh, Ladjel Bellatreche, Selma Bouarar, Ahcene BoukorcaAbstract:Abstract In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In data-centric applications, Database Management Systems are one of the major energy consumers when executing complex queries involving very large databases. Several initiatives have been proposed to deal with this issue, covering both the hardware and software dimensions. They can be classified in two main approaches assuming that either (a) the database is already deployed on a given platform, or (b) it is not yet deployed. In this study, we focus on the first set of initiatives with a particular interest in physical design, where optimization structures (e.g., indexes, materialized views) are selected to satisfy a given set of non-functional requirements such as Query Performance for a given workload. In this paper, we first propose an initiative, called Eco-Physic, which integrates the energy dimension into the physical design when selecting materialized views, one of the redundant optimization structures. Secondly, we provide a multi-objective formalization of the materialized view selection problem, considering two non-functional requirements: Query Performance and energy consumption while executing a given workload. Thirdly, an evolutionary algorithm is developed to solve the problem. This algorithm differs from the existing ones by being interactive, so that database administrators can adjust some energy sensitive parameters at the final stage of the algorithm execution according to their specifications. Finally, intensive experiments are conducted using our mathematical cost model and a real device for energy measurements. Results underscore the value of our approach as an effective way to save energy while optimizing queries through materialized views structures.
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evaluation of materialized view indexing in data warehousing environments
Lecture Notes in Computer Science, 2000Co-Authors: Ladjel Bellatreche, Kamalakar Karlapalem, Qing LiAbstract:Index selection is one of the most important decisions in designing a data warehouse (DW). In this paper, we present a framework for a class of graph join indices used for indexing queries defined on materialized views. We develop storage cost needed for these indices, and Query processing strategies using them. We formulate the graph join index selection problem, and present algorithms which can provide good Query Performance under limited storage space for the indices. We also evaluate these algorithms to show their utilities by using an example taken from Informix white paper.
Wei Wang - One of the best experts on this subject based on the ideXlab platform.
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path materialization revisited an efficient storage model for xml data
Australasian Database Conference, 2002Co-Authors: Haifeng Jiang, Wei WangAbstract:XML is emerging as a new major standard for representing data on the world wide web. Several XML storage models have been proposed to store XML data in different database management systems. The unique feature of model-mapping-based approaches is that no DTD information is required for XML data storage. In this paper, we present a new model-mapping-based storage model, called XParent. Unlike the existing work on model-mapping-based approaches that emphasized on converting XML documents to/from database schema and translation of XML queries into SQL queries, in this paper, we focus ourselves on the effectiveness of storage models in terms of Query processing. We study the key issues that affect Query Performance, namely, storage schema design (storing XML data across multiple tables) and path materialization (storing path information in databases). We show that similar but different storage models significantly affect Query Performance. A Performance study is conducted using three data sets and Query sets. The experimental results are presented.
Cristina Dutra De Aguiar Ciferri - One of the best experts on this subject based on the ideXlab platform.
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physical data warehouse design on nosql databases
International Conference on Enterprise Information Systems, 2016Co-Authors: Lucas C Scabora, Jaqueline J Brito, Ricardo Rodrigues Ciferri, Cristina Dutra De Aguiar CiferriAbstract:Nowadays, data warehousing and online analytical processing (OLAP) are core technologies in business intelligence and therefore have drawn much interest by researchers in the last decade. However, these technologies have been mainly developed for relational database systems in centralized environments. In other words, these technologies have not been designed to be applied in scalable systems such as NoSQL databases. Adapting a data warehousing environment to NoSQL databases introduces several advantages, such as scalability and flexibility. This paper investigates three physical data warehouse designs to adapt the Star Schema Benchmark for its use in NoSQL databases. In particular, our main investigation refers to the OLAP Query processing over column-oriented databases using the MapReduce framework. We analyze the impact of distributing attributes among column-families in HBase on the OLAP Query Performance. Our experiments showed how processing time of OLAP queries was impacted by a physical data warehouse design regarding the number of dimensions accessed and the data volume. We conclude that using distinct distributions of attributes among column-families can improve OLAP Query Performance in HBase and consequently make the benchmark more suitable for OLAP over NoSQL databases.
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how does the spatial data redundancy affect Query Performance in geographic data warehouses
Journal of Information and Data Management, 2010Co-Authors: Rodrigo Costa Mateus, Ricardo Rodrigues Ciferri, Valeria Cesario Times, Thiago Luis Lopes Siqueira, Cristina Dutra De Aguiar CiferriAbstract:Geographic Data Warehouses (GDWs) are traditional data warehouses with spatial attributes that are used for defining spatial dimension tables, spatial measures and spatial hierarchies. Non-redundant spatial data warehouse schemas have been recognized as an essential issue in the GDW design. Although the lack of spatial redundancy represents a gain in data storage, it implies in a need for performing expensive join operations to answer a given Query that may refer to one or more Query windows. In this paper, we investigate to what extent the separate storage of spatial and conventional data is recommended in GDW, according to increasing numbers of Query windows. We also investigate if the complexity of the spatial data (i.e. points versus polygons) influences the choice of storing spatial and conventional data in the same or in different dimension tables. Our experimental results indicated that if non-redundant spatial data are represented as point objects, an approach to avoid additional join costs by storing both point data and their descriptive data in a single table should be chosen. The results also showed that redundant GDW schemas introduce a severe drawback, as some spatial analytical queries cannot reuse previously fetched spatial data, impairing Query Performance. Finally, based on the experimental results, we propose in this paper a set of guidelines for the design of logical GDW schemas, called ``Logical GDW Design Guidelines''.
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investigating the effects of spatial data redundancy in Query Performance over geographical data warehouses
Brazilian Symposium on GeoInformatics, 2008Co-Authors: Thiago Luis Lopes Siqueira, Ricardo Rodrigues Ciferri, Valeria Cesario Times, Cristina Dutra De Aguiar CiferriAbstract:1 This work has been supported by the following Brazilian research agencies: CAPES, CNPq, FAPESP, FINEP and INEP. The first two authors also thank the support of the Web-PIDE Project in the context of the Observatory of the Education of the Brazilian Government. Abstract. Geographical Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis. For these, several conceptual and logical data models have been proposed in the literature. However, little attention has been devoted to the study of how spatial data redundancy affects Query Performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high Performance losses. Further, we analyze the indexing issue, aiming at improving Query Performance on a redundant GDW. Comparisons among the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST showed that SB-index significantly improves the elapsed time on Query processing from 25% up to 95%.
Qing Li - One of the best experts on this subject based on the ideXlab platform.
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evaluation of materialized view indexing in data warehousing environments
Lecture Notes in Computer Science, 2000Co-Authors: Ladjel Bellatreche, Kamalakar Karlapalem, Qing LiAbstract:Index selection is one of the most important decisions in designing a data warehouse (DW). In this paper, we present a framework for a class of graph join indices used for indexing queries defined on materialized views. We develop storage cost needed for these indices, and Query processing strategies using them. We formulate the graph join index selection problem, and present algorithms which can provide good Query Performance under limited storage space for the indices. We also evaluate these algorithms to show their utilities by using an example taken from Informix white paper.
Kamalakar Karlapalem - One of the best experts on this subject based on the ideXlab platform.
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evaluation of materialized view indexing in data warehousing environments
Lecture Notes in Computer Science, 2000Co-Authors: Ladjel Bellatreche, Kamalakar Karlapalem, Qing LiAbstract:Index selection is one of the most important decisions in designing a data warehouse (DW). In this paper, we present a framework for a class of graph join indices used for indexing queries defined on materialized views. We develop storage cost needed for these indices, and Query processing strategies using them. We formulate the graph join index selection problem, and present algorithms which can provide good Query Performance under limited storage space for the indices. We also evaluate these algorithms to show their utilities by using an example taken from Informix white paper.
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algorithms for materialized view design in data warehousing environment
Very Large Data Bases, 1997Co-Authors: Jian Yang, Kamalakar KarlapalemAbstract:Selecting views to materialize is one of the most important decisions in designing a data warehouse. In this paper, we present a framework for analyzing the issues in selecting views to materialize so as to achieve the best combination of good Query Performance and low view maintenance. We first develop a heuristic algorithm which can provide a feasible solution based on individual optimal Query plans. We also map the materialized view design problem as O-l integer programming problem, whose solution can guarantee an optimal solution.
