S. Chaudhuri and U. Dayal. Data warehousing and olap for decision support. In SIGMOD, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....13: Finding the optimal matching tree for a given query. 47 14: Recursive algorithm for evaluating cube queries. 48 15: Answering Query q = COUNT(2, [1,3], 1, 3 ) 48 16: Regular sampling . 53 17: Ranks in different layers ....

....Experimental evaluations of our CubiST prototype implementation have demonstrated its superior run time performance and scalability when compared to existing OLAP systems. CHAPTER 1 OLAP QUERIES AND DATA CUBES Data warehouses and related on line analytical processing (OLAP) technologies [1, 2] continue to receive strong interest from the research community as well as from industry. A warehouse contains data from a number of independent sources, integrated and cleansed to support clients who wish to analyze the data for trends and anomalies. The decision support is provided by OLAP ....

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S. Chaudhuri and U. Dayal, "Data Warehousing and OLAP for Decision Support," SIGMOD Record (ACM Special Interest Group on Management of Data), vol. 26, pp. 507-508, 1997.

....model and to multidimensional arrays. 1. Introduction In recent database trends, data warehouses come to fill a gap in the field of querying large, distributed and frequently updated systems. Most researchers and developers share the same general vision of what a data warehouse is [19] [3]. Data are extracted from several data sources, cleansed, customized and inserted into the data warehouse. The logical structure and semantics of the data, or else Enterprise Model, is stored in an Information Directory. Next, the data warehouse data can be filtered, aggregated and stored in ....

....which one can change of the dimensional orientation of the cube, e.g. swapping the rows and columns, or moving one of the row dimensions into the column dimension, etc. 6] 13] Two are the basic architectures for storing data in an OLAP database: ROLAP and MOLAP. ROLAP (Relational OLAP) [3] is based on a relational database server, extended with capabilities such as extended aggregation and partitioning of data [8] The schema of the database can be a star, snowflake, or fact constellation schema [3] On the other hand, MOLAP (Multidimensional OLAP) is based on pure ....

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S. Chaudhuri, U.Dayal, "Data warehousing and OLAP for Decision Support", Tutorials of 22 nd VLDB Conference, 1996.

.... between Logic Programming and Databases has long been recognized as a very fruitful area of research, witness for example [12, 14, 17] Accessing large amounts of loosely related information is one of the issues in data warehousing; some approaches taken to tackle this problem are discussed in [6, 7, 13]. A general approach consists in interposing a mediator[21, 22] between the user and the data sources. The mediator is often formalized and implemented in a variant of a Logic Programming language such as Prolog. Many issues involved in using Logic to integrate different information repositories ....

Surajit Chaudhuri and Umeshwar Dayal. Data warehousing and olap for decision support (tutorial). In Joan Peckham, editor, SIGMOD 1997.

.... between Logic Programming and Databases has long been recognized as a very fruitful area of research, witness for example [13, 15, 21] Accessing large amounts of loosely related information is one of the issues in data warehousing; some approaches taken to tackle this problem are discussed in [7, 8, 14]. A general approach consists in interposing a mediator [27] between the user and the data sources. The mediator is often formalized and implemented in a variant of a Logic Programming language such as Prolog. Many issues involved in using Logic to integrate di#erent information repositories or to ....

Surajit Chaudhuri and Umeshwar Dayal. Data warehousing and olap for decision support (tutorial). In Peckham [23], pages 507--508. 1

....I O when the ST fits into memory. We have implemented CubiST and our initial set of experiments has demonstrated significant improvements in performance and scalability over existing ROLAP approaches. 1. Introduction Data warehouses and related OLAP (On line Analytical Processing) technologies [6, 7] continue to receive strong interest from the research community as well as from industry. A warehouse contains data from a number of independent sources, integrated and cleansed to support clients who wish to analyze the data for trends and anomalies. The decision support is provided by OLAP ....

S. Chaudhuri and U. Dayal, "Data Warehousing and OLAP for Decision Support," SIGMOD Record (ACM Special Interest Group on Management of Data), 26:2, pp. 507-508, 1997.

....the notion of comparison and it only uses simple thresholds, like support. 2. 2 Iceberg Cube Computation Introduced in [GBLP96] GCB 97] data cubes have received much research attention for its materialization of multi dimensional and multi level data allows on line analytical processing (OLAP) [CD97] and data mining. However, materialization of the whole cube lattice suffers from the curse of dimensionality. To combat the expensive cost, many [AAD 96, HRU96, ZDN97, RS97, FSM 98, BR99] have proposed to (1) selectively materialize some cuboids in a data cube [HRU96] where a cuboid is a ....

Surajit Chaudhuri, Umeshwar Dayal. Data Warehousing and OLAP for Decision Support (Tutorial). In Proceedings ACM SIGMOD International Conference on Management of Data (SIGMOD'97), May 13-15, 1997, Tucson, Arizona, USA.

.... seekers work in an iterative fashion, starting with broad queries and continually refining them based on feedback and domain knowledge (see [OJ93] for a user study in a business data processing environment) Unfortunately, current data processing applications such as decision support querying [CD97] and scientific data visualization [A 96] typically run in batch mode: the user enters a request, the system runs for a long time without any feedback, and then returns an answer. These queries typically scan large amounts of data, and the resulting long delays disrupt the user s ....

S. Chaudhuri and U. Dayal. Data warehousing and OLAP for decision support. In SIGMOD, 1997.

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S. Chaudhuri and U. Dayal. Data warehousing and olap for decision support. In SIGMOD, 1997.

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S. Chaudhuri, U. Dayal, Data Warehousing and OLAP for Decision Support, Tutorials of 22th VLDB Conf., Bombay, 1996.

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S. Chaudhuri and U. Dayal. Data warehousing and OLAP for decision support. In Tutorial of the 22nd International Conference on Very Large Data Bases, Mumbai, India, 1996.

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Surajit Chaudhuri and Umeshwar Dayal. Data warehousing and olap for decision support (tutorial). In Peckham [23], pages 507-508. 1

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