SRQL: Sorted Relational Query Language

Ramakrsihnan, R.; Donjerkovic, D.; Ranganathan, Anand; Beyer, Kevin; Krishnaprasad, Muralidhar

doi:10.1109/ssdm.1998.688114

Cited by 37 publications

(45 citation statements)

References 5 publications

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“…Sequential data analysis has been researched since several years with respect to storage, e.g., [25][26][27][28]. In [27] sequences are modeled by an enhanced abstract data type, in an objectrelational model, whereas in [25] sequences are modeled as sorted relations.…”

Section: Formal Model Of Time Point-based Sequential Data For Olap-limentioning

confidence: 99%

Formal model of time point-based sequential data for OLAP-like analysis

Bębel¹,

Morzy²,

Królikowski³

et al. 2014

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Numerous nowadays applications generate huge sets of data, whose natural feature is order, e.g,. sensor installations, RFID devices, workflow systems, Website monitors, health care applications. By analyzing the data and their order dependencies one can acquire new knowledge. However, nowadays commercial BI technologies and research prototypes allow to analyze mostly set oriented data, neglecting their order (sequential) dependencies. Few approaches to analyzing data of sequential nature have been proposed so far and all of them lack a comprehensive data model being able to represent and analyze sequential dependencies. In this paper, we propose a formal model for time point-based sequential data. The main elements of this model include an event and a sequence of events. Measures are associated with events and sequences. Measures are analyzed in the context set up by dimensions in an OLAP-like manner by means of the set of operations. The operations in our model are categorized as: operations on sequences, on dimensions, general operations, and analytical functions.

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Section: Formal Model Of Time Point-based Sequential Data For Olap-limentioning

confidence: 99%

Formal model of time point-based sequential data for OLAP-like analysis

Bębel¹,

Morzy²,

Królikowski³

et al. 2014

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…This simple example is easily expressed in all the pattern languages proposed in the past [23,29,30,.…”

Section: Comparison With Other Languagesmentioning

confidence: 99%

“…The limitations of SQL with time-series queries are well-known, and have been the focus of many database research projects aiming at supporting timeseries analysis and the search for interesting patterns in stored sequences [16,23,29,30,. Informix [16] was the first among commercial DBMSs to provide special libraries for time-series, that they named datablades; these libraries consist of functions that can be called in SQL queries.…”

Section: Introductionmentioning

confidence: 99%

“…SEQUIN's special algebra makes the optimization of sequence queries possible, but optimization between sequence queries and set queries is not supported; also its expressive power is still too limited for many application areas. To address these problems, SRQL [23] augments relational algebra with a sequential model based on sorted relations. Thus sequences are expressed in the same framework as sets, enabling more efficient optimization of queries that involve both [23].…”

Section: Introductionmentioning

confidence: 99%

“…To address these problems, SRQL [23] augments relational algebra with a sequential model based on sorted relations. Thus sequences are expressed in the same framework as sets, enabling more efficient optimization of queries that involve both [23]. SRQL also extends SQL with some constructs for querying sequences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Support for Time Series Queries in Data Stream Management Systems

Bai

Luo

Thakkar

et al.

Stream Data Management

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There is much current interest in supporting continuous queries on data streams using generalizations of database query languages, such as SQL. The research challenges faced by this approach include (i) overcoming the expressive power limitations of database languages on data stream applications, and (ii) providing query processing and optimization techniques for the data stream execution environment that is so different from that of traditional databases. In particular, SQL must be extended to support sequence queries on time series, and to overcome the loss of expressive power due to the exclusion of blocking query operators. Furthermore, the query processing techniques of relational databases must be replaced with techniques that optimize execution of time-series queries and the utilization of main memory. The Expressive Stream Language for Time Series (ESL-TS) and its query optimization techniques solve these problems efficiently and are part of the data stream management system prototype developed at UCLA.

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