Modeling and processing of time interval data for data-driven decision support

Meisen, Philipp; Recchioni, Marco; Meisen, Tobias; Schilberg, Daniel; Jeschke, Sabina

doi:10.1109/smc.2014.6974378

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…The tests show that using roaring bitmaps can increase the performance by a factor of 25. With respect to results based on JavaEWAH shown in [11] using roaring bitmaps is promising.…”

Section: Performancementioning

confidence: 96%

“…minutes) is used for the time dimension [7]. Additionally, the performance of the OLAP system decreases tremendously [13] and some systems are not capable of handling the amount of data necessary to be created in memory when querying time interval data [11].…”

Section: A Aggregation and Many-to-many Relationshipsmentioning

confidence: 99%

“…Entities defined for two-step aggregation technique. 3 The insertion process is introduced in detail in [11]. We refer the interested reader to that paper for more insights.…”

Section: B Descriptor Timeaxis and Factdescriptor Indexmentioning

confidence: 99%

“…This paper is organized as follows: First, we provide a short recap of the time interval data analysis model (TIDAMODEL) presented in detail in [11]. Next, we present the problems occurring while aggregating time interval data from a semantic and temporal point of view and we introduce our two-step aggregation technique.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of time interval data, the time axis and the time dimension are of special interest. In this paper, we use the TIDAMODEL introduced in [11]. The model is defined by a 5-tuple (5, 6, W, 0, ') in which 5 denotes the time interval database, 6 the set of descriptors, W the time axis, 0 the set of measures, and ' the set of dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Bitmap-Based On-line Analytical Processing of Time Interval Data

Meisen

Keng

Meisen

et al. 2015

2015 12th International Conference on Information Technology - New Generations

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On-line analytical processing is in the focus of research over the last couple decades. Several papers dealing with summarizability problems, cube computations, query languages, fact-dimension relationships or different types of hierarchies have been published. Nowadays, analyzing time interval data became ubiquitous. Nevertheless, the use of established, reliable, and proven technologies like OLAP is desirable in this respect.In this paper, we present an OLAP system capable to process time interval data. The system is based on bitmaps, enabling performant selection and fast aggregation. Moreover, we introduce a two-step aggregation technique, which enables the calculation of relevant measures in the context of time interval data. We evaluate the performance of our system using different bitmap implementations and a real-world data set. To our knowledge, there are no other systems available enabling OLAP and providing correct results considering the summarizability of time interval data.

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“…The tests show that using roaring bitmaps can increase the performance by a factor of 25. With respect to results based on JavaEWAH shown in [11] using roaring bitmaps is promising.…”

Section: Performancementioning

confidence: 96%

Section: A Aggregation and Many-to-many Relationshipsmentioning

confidence: 99%

“…Entities defined for two-step aggregation technique. 3 The insertion process is introduced in detail in [11]. We refer the interested reader to that paper for more insights.…”

Section: B Descriptor Timeaxis and Factdescriptor Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations