Materialized View Selection for Multi-cube Data Models

Shukla, Amit; Deshpande, Prasad M.; Naughton, Jeffrey F.

doi:10.1007/3-540-46439-5_19

Cited by 98 publications

(152 citation statements)

References 5 publications

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“…Materialized view selection for multidimensional datasets is discussed in [8]. The approach used a fast heuristic algorithm PBS, (Pick by size) to select desired views for materialization, where PBS selects aggregating views in increasing order of their size.…”

Section: Background and Preliminariesmentioning

confidence: 99%

CB-Pattern Trees : Identifying Distributed Nodes Where Materialization is Available

Dhanasree¹,

Bindu²

2016

IJDKP

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When a query requesting materialization arrives at a distributed node it checks in its cache and if the materialization is available answers the query. What if materialization is not available-the node communicates the query in the network until a node answering the requested materialization is available. This type of network communication increases the number of query forwarding's between nodes. The aim of this paper is to reduce the multiple redirects. In this paper we propose a new CB-pattern tree indexing to identify the exact distributed node where the needed materialization is available.

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Section: Background and Preliminariesmentioning

confidence: 99%

CB-Pattern Trees : Identifying Distributed Nodes Where Materialization is Available

Dhanasree¹,

Bindu²

2016

IJDKP

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“…For this kind of modeling, we adopt the terminology of [31] and we refer to that virtual fact schema as a 'multi-cube' and to the columns used to join the original fact tables as the 'join dimensions'. The construction of multi-cubes is pictorially depicted in Figure 5(a) and is formally defined by the following definition.…”

Section: Multi-cubesmentioning

confidence: 99%

Multidimensional content eXploration

et al. 2008

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Content Management Systems (CMS) store enterprise data such as insurance claims, insurance policies, legal documents, patent applications, or archival data like in the case of digital libraries. Search over content allows for information retrieval, but does not provide users with great insight into the data. A more analytical view is needed through analysis, aggregations, groupings, trends, pivot tables or charts, and so on. Multidimensional Content eXploration (MCX) is about effectively analyzing and exploring large amounts of content by combining keyword search with OLAP-style aggregation, navigation, and reporting. We focus on unstructured data or generally speaking documents or content with limited metadata, as it is typically encountered in CMS. We formally present how CMS content and metadata should be organized in a well-defined multidimensional structure, so that sophisticated queries can be expressed and evaluated. The CMS metadata provide traditional OLAP static dimensions that are combined with dynamic dimensions discovered from the analyzed keyword search result, as well as measures for document scores based on the link structure between the documents. In addition, we provide means for multidimensional content exploration through traditional OLAP rollupdrilldown operations on the static and dynamic dimensions, solutions for multi-cube analysis and dynamic navigation of the content. We present our prototype, called DBPubs, which stores research publications as documents that can be searched and -most importantly-analyzed, and explored. Finally, we present experimental results of the efficiency and effectiveness of our approach.

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“…It also proposes the heuristic that a view that is not optimal for any query in the query workloads can never be a member of an optimal materialized view set. Since they are beyond the scope of this paper and tremendous research has already been done on the subject, we won't deal with the heuristics further at this time [5] [2] [11].…”

Section: Generating Candidate View Spacementioning

confidence: 99%

Using Relational Database Constraints to Design Materialized Views in Data Warehouses

Lee

Chang

Lee

2004

Advanced Web Technologies and Applications

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Abstract. Queries to data warehouses often involve hundreds of complex aggregations over large volumes of data, and so it is infeasible to compute these queries by scanning the data sources each time. Data warehouses therefore build a large number of materialized views to increase system performance. However, materialized views need to be immediately updated when its sources are changed, leading to a possible decrease in system performance. The goal of the materialized view selection problem is to select an appropriate set of views that minimize total query response time as well as the view maintenance cost. In this paper, we develop a solution for identifying the candidate view space of materialization. In particular, we present algorithms for generating a unionview and a partial-view, both of which are good candidates for materialization. The proposed candidate view space guarantees to find a polynomial bounded set of optimal views, and any selection algorithm from previous research, e.g. greedy algorithm, can be ran on the candidate view space to find the optimal materialized views.

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Materialized View Selection for Multi-cube Data Models

Cited by 98 publications

References 5 publications

CB-Pattern Trees : Identifying Distributed Nodes Where Materialization is Available

CB-Pattern Trees : Identifying Distributed Nodes Where Materialization is Available

Multidimensional content eXploration

Using Relational Database Constraints to Design Materialized Views in Data Warehouses

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