Developing a DataBlade for a new index

Bliujute, R.; Šaltenis, Simonas; Slivinskas, Giedrius; Jensen, Christian S.

doi:10.1109/icde.1999.754947

Cited by 19 publications

(9 citation statements)

References 15 publications

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“…Example systems include IBM, DB2 UDB (IBM 1995), Informix Universal Server (Informix 1998), Oracle8 (Oracle 1999), and Predator [94]. Descriptions of sample component developments can be found in [18,38].…”

Section: Component-oriented Dbms: Personalizing Data Managementmentioning

confidence: 99%

“…Taking Google's GFS and Map-Reduce papers as rough technical specifications, open-source equivalents were developed, and the Apache Hadoop Map-Reduce platform, and its underlying file system HDFS emerged. 18 Microsoft's technologies include a parallel runtime system called Dryad [69], and two higher-level programming models, DryadLINQ [110] and the SQL-like SCOPE language [27]. The Hadoop community developed a set of higher-level declarative languages for writing queries and data analysis pipelines that are compiled into Map-Reduce jobs, and then executed on the Hadoop MapReduce platform.…”

Section: Parallel Model For Implementing Data Processing Functionsmentioning

confidence: 99%

See 1 more Smart Citation

Big Data Management: What to Keep from the Past to Face Future Challenges?

2017

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The emergence of new hardware architectures, and the continuous production of data open new challenges for data management. It is no longer pertinent to reason with respect to a predefined set of resources (i.e., computing, storage and main memory). Instead, it is necessary to design data processing algorithms and processes considering unlimited resources via the ''pay-as-you-go'' model. According to this model, resources provision must consider the economic cost of the processes versus the use and parallel exploitation of available computing resources. In consequence, new methodologies, algorithms and tools for querying, deploying and programming data management functions have to be provided in scalable and elastic architectures that can cope with the characteristics of Big Data aware systems (intelligent systems, decision making, virtual environments, smart cities, drug personalization). These functions, must respect QoS properties (e.g., security, reliability, fault tolerance, dynamic evolution and adaptability) and behavior properties (e.g., transactional execution) according to application requirements. Mature and novel system architectures propose models and mechanisms for adding these properties to new efficient data management and processing functions delivered as services. This paper gives an overview of the different architectures in which efficient data management functions can be delivered for addressing Big Data processing challenges.

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Section: Component-oriented Dbms: Personalizing Data Managementmentioning

confidence: 99%

Section: Parallel Model For Implementing Data Processing Functionsmentioning

confidence: 99%

Big Data Management: What to Keep from the Past to Face Future Challenges?

2017

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“…More and more object-relational database systems implement extensible indexing frameworks [3,4,10,38], and the RI-tree successfully has been integrated in one of those systems already [28]. Starting with Version 7.1, the IBM DB2 Universal Database Server has been extended by an extensible index interface [2,12].…”

Section: Fig 1 Period Examplementioning

confidence: 99%

“…4 provides an example for the RI-tree. Let us assume the intervals (2,13) for Mary, (4,23) for John, (10,21) for Bob, and (21,30) for Ann (Fig. 4a).…”

Section: Original Optimal Relational Mappingmentioning

confidence: 99%

Efficient interval management using object-relational database servers

Brochhaus

Enderle

Schlosser

et al. 2005

Informatik Forsch. Entw.

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User-defined data types such as intervals require specialized access methods to be efficiently searched and queried. As database implementors cannot provide appropriate index structures and query processing methods for each conceivable data type, present-day object-relational database systems offer extensible indexing frameworks that enable developers to extend the set of built-in index structures by custom access methods. Although these frameworks permit a seamless integration of user-defined indexing techniques into query processing they do not facilitate the actual implementation of the access method itself. In order to leverage the applicability of indexing frameworks, relational access methods such as the Relational Interval Tree (RI-tree), an efficient index structure to process interval intersection queries, mainly rely on the functionality, robustness and performance of built-in indexes, thus simplifying the index implementation significantly. To investigate the behavior and performance of the recently released IBM DB2 indexing framework we use this interface to integrate the RI-tree into the DB2 server. The standard implementation of the RI-tree, however, does not fit to the narrow corset of the DB2 framework which is restricted to the use of a single index only. We therefore present our adaptation of the original two-tree technique to the single index constraint as well as an approximate adaptation which conceptually only needs a single index. As experimental results with interval intersection queries show, the plugged-in access methods deliver excellent performance compared to other techniques.

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“…Most ORDBMSs, including Oracle [21,24], IBM DB2 [5,12] or Informix IDS/UDO [4,13], provide extensibility interfaces in order to enable database developers to seamlessly integrate custom object types and predicates within the declarative DDL and DML. These interfaces form a necessary prerequisite for the seamless embedding of spatial objects and the intersect predicate into off-the-shelf ORDBMSs.…”

Section: Query Processingmentioning

confidence: 99%