Complex aggregation at multiple granularities

Ross, Kenneth A.; Srivastava, Divesh; Chatziantoniou, Damianos

doi:10.1007/bfb0100990

Cited by 23 publications

(15 citation statements)

References 4 publications

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“…The methods of computing these two types of MFCubes are mainly discussed in [15] and they can be computed with many simple data-cube optimizing algorithms, for example, those set out in [1,2,18]. However, for Holistic MF-Cubes, it is pointed out in [15] that there is no efficient technique available that reaches beyond the straightforward method of first partitioning the data cubes and then computing all the 2 n granularities separately. A major problem of this method is that the required computer storage space can expand exponentially if all the granularities are pre-computed, especially when the cube has many CUBE BY attributes.…”

Section: Motivationmentioning

confidence: 99%

“…Related work on MF-Cube queries reported in [15] reveals that the computations result in a great many redundant data items being lodged in memory and an increase in I/O costs. In fact, part materialization is a moderate method for reducing the size of data cubes.…”

Section: Motivationmentioning

confidence: 99%

“…There are many decision support systems (DSS) based on dealing with MDD, including online analytical processing (OLAP) in data warehousing, relation-OLAP, and multidimensional-OLAP (MOLAP). Because of the multidimensional nature of MDD sets, a data cube that models the MDD is constructed in order to view the included data [1,2,14,15,18]. Such a data cube model is not only of benefit in the visualization of MDD, but it is also useful in computing aggregations at multi-granularities and in providing answers to users' queries as soon as possible.…”

Section: Multi-dimensional Data and Data Cube Queriesmentioning

confidence: 99%

“…In [15], a multi-feature cube (MF-cube) was employed to compute a complex data-mining query and it is evident that it can efficiently answer many such queries. The authors classified MF-Cubes into three categories based on the extent to which finer granularity results can be used to compute coarser granularity results (as shown in Fig.…”

Section: Motivationmentioning

confidence: 99%

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Strategies for complex data cube queries

2008

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This paper proposes a computation method for holistic multi-feature cube (MF-Cube) queries based on the characteristics of MF-Cubes. Three simple yet efficient strategies are designed to optimize the dependent complex aggregate at multiple granularities for a complex datamining query within data cubes. One strategy is the computation of Holistic MF-Cube queries using the PDAP (Part Distributive Aggregate Property). More efficiency is gained by another strategy, that of dynamic subset data selection (the iceberg query technique), which reduces the size of the materialized data cubes. To extend this efficiency further, the second approach may adopt the chunk-based caching technique that reuses the output of previous queries. By combining these three strategies, we design an algorithm called the PDIC (Part Distributive Iceberg Chunk). We experimentally evaluate this algorithm using synthetic and real-world datasets and demonstrate that our approach delivers up to approximately twice the performance efficiency of traditional computation methods.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Multi-dimensional Data and Data Cube Queriesmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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Strategies for complex data cube queries

2008

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“…Account the critical problem of memory limitation. In [15] proposed analytical and experimental performance study shows that APIC and BUC are promising candidates for scalable computation and the best efficiency of APIC.…”

Section: Iceberg Cubementioning

confidence: 99%

Methods for Evaluating Iceberg Queries

Padmapriya¹,

Shanmugapriya²

2013

IJCA

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Iceberg queries are a special case of SQL queries involving GROUP BY and HAVING clauses, wherein the answer set is small relative to the database size. Iceberg queries have been recently identified as important queries for many applications. Queries can be characterized by their huge input-small output. The iceberg refers to the input, and the tip of it refers to the output. This paper is going to present some of the existing iceberg query processing using data mining.

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A session‐oriented approach in modeling hierarchies of streams

Chatziantoniou

Anagnostopoulos

2007

Softw Pract Exp

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Data stream systems became recently the focus of intense research activity. Sensor measurements, financial data, network packets and log files can all be seen as data streams. The ability to query data streams is of increasing importance and has been identified as a crucial element for modern organizations and agencies. This article identifies an interesting class of applications where stream sessions may be organized into a hierarchical fashion—i.e. sessions may consist of subsessions. For example, log streams from call centers belong to different call sessions and call sessions consist of services subsessions. We may want to monitor statistics and perform accounting at any level on this hierarchy, relative to any other (higher) level (e.g. monitoring the average service session per call vs the average service session for the entire system). We argue that data streams of this kind have rich procedural semantics—i.e. behaviour—and therefore a semantically rich model should be used: a session may be defined by opening and closing conditions, may have data and methods and may consist of subsessions. We propose a simple conceptual model based on the notion of ‘session’—similar to a class in an object‐oriented environment having lifetime semantics. Queries on top of this schema can be formulated via HSA (hierarchical stream aggregate) expressions. We give an algorithm describing how stream data ‘flow down’ session hierarchies and discuss potential evaluation and optimization techniques for HSAs. We finally present NESTREAM, a prototype implementation incorporating many of these concepts. Copyright © 2007 John Wiley & Sons, Ltd.

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Complex aggregation at multiple granularities

Cited by 23 publications

References 4 publications

Strategies for complex data cube queries

Strategies for complex data cube queries

Methods for Evaluating Iceberg Queries

A session‐oriented approach in modeling hierarchies of streams

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