OLAP on sequence data

Lo, Eric; Kao, Ben; Ho, Wai-Shing; Lee, Sau Dan; Chui, Chun Kit; Cheung, David W.

doi:10.1145/1376616.1376682

Cited by 69 publications

(80 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combine({ e 1 , e 2 , e 5 , e 3 , e 6 , e 9 , e 11 }) = e 1 , e 2 , e 5 , e 3 , e 9 , e 11 , e 6 We assume the total order on all events from the set of initial sequences.…”

Section: Combine(s) -Creates a New Sequence With Events Of Allmentioning

confidence: 99%

“…[6,15,16] focus on storage and analysis of event based sequences. [6] propose the set of operators for a query language for the purpose of analyzing patterns.…”

Section: Formal Model Of Time Point-based Sequential Data For Olap-limentioning

confidence: 99%

“…Some route changes may be then made on-line. In advanced public transportation infrastructures, e.g., Washington, Hong Kong [3][4][5][6] passengers use intelligent cards. Travels are automatically detected by various devices, generating tracking records.…”

Section: Introductionmentioning

confidence: 99%

“…The research and technological advancements in the area of sequential data analysis have just been launched. Some extensions to traditional OLAP functionality have been proposed in the research literature, commonly known as Sequential OLAP (S-OLAP) [6,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…The advancements, e.g., [6,[14][15][16][17] focus on analyzing time-point based sequential data. Conversely, [2,18] focus on interval based sequential data but only from a storage point of view.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Formal model of time point-based sequential data for OLAP-like analysis

Bębel¹,

Morzy²,

Królikowski³

et al. 2014

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Numerous nowadays applications generate huge sets of data, whose natural feature is order, e.g,. sensor installations, RFID devices, workflow systems, Website monitors, health care applications. By analyzing the data and their order dependencies one can acquire new knowledge. However, nowadays commercial BI technologies and research prototypes allow to analyze mostly set oriented data, neglecting their order (sequential) dependencies. Few approaches to analyzing data of sequential nature have been proposed so far and all of them lack a comprehensive data model being able to represent and analyze sequential dependencies. In this paper, we propose a formal model for time point-based sequential data. The main elements of this model include an event and a sequence of events. Measures are associated with events and sequences. Measures are analyzed in the context set up by dimensions in an OLAP-like manner by means of the set of operations. The operations in our model are categorized as: operations on sequences, on dimensions, general operations, and analytical functions.

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“…Combine({ e 1 , e 2 , e 5 , e 3 , e 6 , e 9 , e 11 }) = e 1 , e 2 , e 5 , e 3 , e 9 , e 11 , e 6 We assume the total order on all events from the set of initial sequences.…”

Section: Combine(s) -Creates a New Sequence With Events Of Allmentioning

confidence: 99%

“…[6,15,16] focus on storage and analysis of event based sequences. [6] propose the set of operators for a query language for the purpose of analyzing patterns.…”

Section: Formal Model Of Time Point-based Sequential Data For Olap-limentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Formal model of time point-based sequential data for OLAP-like analysis

Bębel¹,

Morzy²,

Królikowski³

et al. 2014

Bulletin of the Polish Academy of Sciences Technical Sciences

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Topic modeling for OLAP on multidimensional text databases: topic cube and its applications

Zhang¹,

Zhai²,

Srivastava³

et al. 2009

Statistical Analysis

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Abstract:As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more desirable to analyze both structured data records and unstructured text data simultaneously. Although online analytical processing (OLAP) techniques have been proven very useful for analyzing and mining structured data, they face challenges in handling text data. On the other hand, probabilistic topic models are among the most effective approaches to latent topic analysis and mining on text data. In this paper, we study a new data model called topic cube to combine OLAP with probabilistic topic modeling and enable OLAP on the dimension of text data in a multidimensional text database. Topic cube extends the traditional data cube to cope with a topic hierarchy and stores probabilistic content measures of text documents learned through a probabilistic topic model. To materialize topic cubes efficiently, we propose two heuristic aggregations to speed up the iterative Expectation-Maximization (EM) algorithm for estimating topic models by leveraging the models learned on component data cells to choose a good starting point for iteration. Experimental results show that these heuristic aggregations are much faster than the baseline method of computing each topic cube from scratch. We also discuss some potential uses of topic cube and show sample experimental results.

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MiTexCube: MicroTextCluster Cube for online analysis of text cells and its applications

Zhang

Zhai

2012

Statistical Analysis

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Abstract:A fundamental problem of multidimensional text database analysis is efficient and effective support of various kinds of online applications, such as summarizing the content of a text cell or comparing the contents across multiple text cells. In this paper, we propose a new infrastructure called MicroTextCluster Cube (or MiTexCube) to support efficient online text analysis on multidimensional text databases by introducing micro-clusters of text documents as a compact representation of text content. Experimental results on real multidimensional text databases show that (i) MiTexCube can be materialized efficiently with reasonable overhead in space, and (ii) applications based on the proposed materialized MiTexCube are more efficient than the baseline method of direct analysis based on document units in each cell, without sacrificing much quality of analysis, and MiTexCube naturally accommodates flexible trade-off between efficiency and quality of analysis.

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OLAP on sequence data

Cited by 69 publications

References 23 publications

Formal model of time point-based sequential data for OLAP-like analysis

Formal model of time point-based sequential data for OLAP-like analysis

Topic modeling for OLAP on multidimensional text databases: topic cube and its applications

MiTexCube: MicroTextCluster Cube for online analysis of text cells and its applications

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