Incremental Aggregation on Multiple Continuous Queries

Jin, Chun; Carbonell, Jaime G.

doi:10.1007/11875604_20

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…Several papers have addressed aggregation for temporal and spatio-temporal data including instantaneous temporal aggregation [21], cumulative temporal aggregation [22], [23], [24], histogram-based aggregation [25], and multi-dimensional temporal aggregation [26]. These techniques differ in how a time line is partitioned into time intervals and how an aggregation group is associated with each time instant.…”

Section: Related Workmentioning

confidence: 99%

Aggregating and Disaggregating Flexibility Objects

Šikšnys

Valsomatzis

Hose

et al. 2015

IEEE Trans. Knowl. Data Eng.

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In many scientific and commercial domains we encounter flexibility objects, i.e., objects with explicit flexibilities in a time and an amount dimension (e.g., energy or product amount). Applications of flexibility objects require novel and efficient techniques capable of handling large amounts of such objects while preserving flexibility. Hence, this paper formally defines the concept of flexibility objects (flex-objects) and provides a novel and efficient solution for aggregating and disaggregating flex-objects. Out of the broad range of possible applications, this paper will focus on smart grid energy data management and discuss strategies for aggregation and disaggregation of flex-objects while retaining flexibility. This paper further extends these approaches beyond flex-objects originating from energy consumption by additionally considering flex-objects originating from energy production and aiming at energy balancing during aggregation. In more detail, this paper considers the complete life cycle of flex-objects: aggregation, disaggregation, associated requirements, efficient incremental computation, and balance aggregation techniques. Extensive experiments based on real-world data from the energy domain show that the proposed solutions provide good performance while satisfying the strict requirements.

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Section: Related Workmentioning

confidence: 99%

Aggregating and Disaggregating Flexibility Objects

Šikšnys

Valsomatzis

Hose

et al. 2015

IEEE Trans. Knowl. Data Eng.

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“…In this figure extension is occurred by extending d 2 , then d 3 ,d 4 and d 1 The EKA scheme can be generalized to n dimensions using a set of EKA(4)s.…”

Section: A the Four Dimensional Eka Schemementioning

confidence: 99%

“…Methods of aggregation has been studied and commercialized with great success [1]. Data warehouse and aggregates can be recomputed with the addition of new data, but it is quite expensive to recompute the whole process.…”

Section: Introductionmentioning

confidence: 99%

“…Since the degree of modification to base data cube is normally small, incremental aggregation method can be applied for dynamically increasing database datasets [2]. However, recently, the emergence of the data stream processing presents new challenges to compute incremental aggregates over ever-changing data streams [1]. Various data structures are used to compute incremental aggregation efficiently with less time and less storage.…”

Section: Introductionmentioning

confidence: 99%

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Incremental aggregation scheme based on Extendible Karnaugh Arrays

Hasan

Rabbi

Ahsan

2014

2014 17th International Conference on Computer and Information Technology (ICCIT)

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Data is increasing so rapidly that new data warehousing approaches are required to process and analyze data. Aggregation of data incrementally is needed to fast access of data and compute aggregation functions. Multidimensional arrays are generally used for this purpose. But some disadvantages such as address space requirement is large and processing time is comparatively slow in case of aggregation. For this purpose we use Extendible Karnaugh Array (EKA). EKA is an efficient scheme which has better performance than other data structures that we have tested in our research. In this research work we use EKA as basic structure for implementing incremental aggregation of data and evaluate its performance over other approaches. We use Multidimensional Online Analytical Processing (MOLAP) which stores data in optimized multi-dimensional array storage, rather than in a relational database. We create MOLAP data cube using Traditional Multidimensional Array (TMA) and EKA scheme and compare incremental aggregation with Relational Online Analytical Processing (ROLAP). The effective outcome of EKA structure for incremental aggregation on MOLAP structure is shown by some experimental results.

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“…Temporal Aggregation. Several papers have addressed aggregation for temporal and spatio-temporal data including: instantaneous temporal aggregation [3], cumulative temporal aggregation [21,1,11], histogram-based aggregation [6] and multi-dimensional temporal aggregation [4]. These techniques differ in the way how a time line is partitioned into time intervals and how an aggregation group is associated with each time instant.…”

Section: Related Workmentioning

confidence: 99%

Aggregating and Disaggregating Flexibility Objects

Šikšnys

Khalefa

Pedersen

2012

Lecture Notes in Computer Science

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Flexibility objects, objects with flexibilities in time and amount dimensions (e.g., energy or product amount), occur in many scientific and commercial domains. Managing such objects with existing DBMSs is infeasible due to the complexity, data volume, and complex functionality needed, so a new kind of flexibility database is needed. This paper is the first to consider flexibility databases. It formally defines the concept of flexibility objects (flex-objects), and provide a novel and efficient solution for aggregating and disaggregating flex-objects. This is important for a range of applications, including smart grid energy management. The paper considers the grouping of flex-objects, alternatives for computing aggregates, the disaggregation process, their associated requirements, as well as efficient incremental computation. Extensive experiments based on data from a real-world energy domain project show that the proposed solution provides good performance while still satisfying the strict requirements.

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Incremental Aggregation on Multiple Continuous Queries

Cited by 7 publications

References 17 publications

Aggregating and Disaggregating Flexibility Objects

Aggregating and Disaggregating Flexibility Objects

Incremental aggregation scheme based on Extendible Karnaugh Arrays

Aggregating and Disaggregating Flexibility Objects

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