Efficient computation of subqueries in complex OLAP

Akinde, Michael O.; Böhlen, Michael H.

doi:10.1109/icde.2003.1260790

Cited by 18 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper presents a new aggregation operator, the Temporal Multi-Dimensional Aggregation (TMDA) operator, that leverages recent advances in multi-dimensional query processing [1][2][3] to apply to interval-valued data. The TMDA operator generalizes a variety of previously proposed aggregation operators.…”

Section: Resultsmentioning

confidence: 99%

Multi-dimensional Aggregation for Temporal Data

Böhlen

Gamper

Jensen

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state. This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses that the data holds for each point in the interval, as well as the case where the data holds only for the entire interval, but must be adjusted to apply to sub-intervals. The paper reports on an implementation of the new operator and on an empirical study that indicates that the operator scales to large data sets and is competitive with respect to other temporal aggregation algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Multi-dimensional Aggregation for Temporal Data

Böhlen

Gamper

Jensen

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…N CI H P T Jan 137 P1 300 [1,3] Tom 138 P1 400 [1,4] Tom 147 P1 400 [5,6] Ann 139 P1 300 [1,6] Tom 140 P2 200 [2,3] Jan 142 P2 600 [2,5] Note that almost all temporal data models proposed by the temporal database community assume that all attributes have the constant characteristic. This applies to the so-called point-based data models [13,32] and to models where snapshot equivalent [20,23] relations are treated as being identical.…”

Section: Emplmentioning

confidence: 99%

“…. ; s l i by a (heterogeneous) bag S. 2 The value UNDEF belongs to each domain in Á and means that the function is not defined.…”

Section: Support For Multiple Attribute Characteristicsmentioning

confidence: 99%

An algebraic framework for temporal attribute characteristics

Böhlen

Gamper

Jensen

2006

Ann Math Artif Intell

View full text Add to dashboard Cite

Most real-world database applications manage temporal data, i.e., data with associated time references that capture a temporal aspect of the data, typically either when the data is valid or when the data is known. Such applications abound in, e.g., the financial, medical, and scientific domains. In contrast to this, current database management systems offer preciously little built-in query language support for temporal data management. This situation persists although an active temporal database research community has demonstrated that application development can be simplified substantially by built-in temporal support. This paper's contribution is motivated by the observation that existing temporal data models and query languages generally make the same rigid assumption about the semantics of the association of data and time, namely that if a subset of the time domain is associated with some data then this implies the association of any further subset with the data. This paper offers a comprehensive, general framework where alternative semantics may co-exist. It supports so-called malleable and atomic temporal associations, in addition to the conventional ones mentioned above, which are termed constant. To demonstrate the utility of the framework, the paper defines a characteristics-enabled temporal algebra, termed CETA, which defines the traditional relational operators in the new framework. This contribution demonstrates that it is possible to provide built-in temporal support while making less rigid assumptions about the data and without jeopardizing the degree of the support. This moves temporal support closer to practical applications.

show abstract

“…Provenance computation can benefit from the de-correlation and un-nesting techniques developed for query optimization [4,9,1,5,16,8,14]. Besides the fact that these approaches are normally only suitable for specific types of sublink queries, the performance gain can be significant.…”

Section: Unn Strategymentioning

confidence: 99%

“…The algebra operates on bags (multi-sets). The cardinality of a tuple is denoted by a superset, as in (1,2) 3 . The schema R/q of a relation R or algebra expression q is a list of attributes (a, b, .…”

Section: Extending the Algebra With Sublinksmentioning

confidence: 99%

Provenance for nested subqueries

Glavic

Alonso

2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

View full text Add to dashboard Cite

Data provenance is essential in applications such as scientific computing, curated databases, and data warehouses. Several systems have been developed that provide provenance functionality for the relational data model. These systems support only a subset of SQL, a severe limitation in practice since most of the application domains that benefit from provenance information use complex queries. Such queries typically involve nested subqueries, aggregation and/or user defined functions. Without support for these constructs, a provenance management system is of limited use.In this paper we address this limitation by exploring the problem of provenance derivation when complex queries are involved. More precisely, we demonstrate that the widely used definition of Why-provenance fails in the presence of nested subqueries, and show how the definition can be modified to produce meaningful results for nested subqueries. We further present query rewrite rules to transform an SQL query into a query propagating provenance. The solution introduced in this paper allows us to track provenance information for a far wider subset of SQL than any of the existing approaches. We have incorporated these ideas into the Perm provenance management system engine and used it to evaluate the feasibility and performance of our approach. Provenance for Nested Subqueries ABSTRACTData provenance is essential in applications such as scientific computing, curated databases, and data warehouses. Several systems have been developed that provide provenance functionality for the relational data model. These systems support only a subset of SQL, a severe limitation in practice since most of the application domains that benefit from provenance information use complex queries. Such queries typically involve nested subqueries, aggregation and/or user defined functions. Without support for these constructs, a provenance management system is of limited use.In this paper we address this limitation by exploring the problem of provenance derivation when complex queries are involved. More precisely, we demonstrate that the widely used definition of Why-provenance fails in the presence of nested subqueries, and show how the definition can be modified to produce meaningful results for nested subqueries. We further present query rewrite rules to transform an SQL query into a query propagating provenance. The solution introduced in this paper allows us to track provenance information for a far wider subset of SQL than any of the existing approaches. We have incorporated these ideas into the Perm provenance management system engine and used it to evaluate the feasibility and performance of our approach.

show abstract

Efficient computation of subqueries in complex OLAP

Cited by 18 publications

References 16 publications

Multi-dimensional Aggregation for Temporal Data

Multi-dimensional Aggregation for Temporal Data

An algebraic framework for temporal attribute characteristics

Provenance for nested subqueries

Contact Info

Product

Resources

About