Main Memory-Based Algorithms for Efficient Parallel Aggregation for Temporal Databases

Gao, Dengfeng; Gendrano, Jose Alvin G.; Moon, Bongki; Snodgrass, Richard T.; Park, Min-Seok; Huang, Bruce C.; Rodrigue, Jim M.

doi:10.1023/b:dapd.0000028553.70337.e1

Cited by 16 publications

(10 citation statements)

References 12 publications

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“…These techniques differ in how a time line is partitioned into time intervals and how an aggregation group is associated with each time instant. The efficient computation of these time intervals poses a great challenge and therefore various techniques that allow computing them efficiently have been proposed [27], [28], [29]. Unfortunately, these techniques only deal with simple data items without flexibilities, making them unsuitable for aggregation of flexobjects.…”

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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“…SB-tree can solve traditional range count problem elegantly, it can be used to solve MCRC query too [11][12][13][14][15]. First we briefly explain SB-tree, figure1 is a SB-tree, which is a traditional B-tree in essence, there is a counter in each non-leaf node which records the number of data elements in each subtree.…”

Section: Preliminary Solutionmentioning

confidence: 99%

An Efficient Index Structure for Range Count Query Based on Improved B-tree

Jinglin¹,

Qi²

2015

IJUNESST

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“…Temporal aggregation queries were well studied and several approaches have been proposed recently [10,13,18,21,25,34,36,38]. However, all these works consider only a single time granularity, where this granularity is usually the same as the granularity used to store the time attributes.…”

Section: Time Granularitymentioning

confidence: 99%

Processing count queries over event streams at multiple time granularities

ínal

Saygın

Ulusoy

2006

Information Sciences

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Management and analysis of streaming data has become crucial with its applications to web, sensor data, network traffic data, and stock market. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over different time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at

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Main Memory-Based Algorithms for Efficient Parallel Aggregation for Temporal Databases

Cited by 16 publications

References 12 publications

Aggregating and Disaggregating Flexibility Objects

Aggregating and Disaggregating Flexibility Objects

An Efficient Index Structure for Range Count Query Based on Improved B-tree

Processing count queries over event streams at multiple time granularities

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