Exegesis of DBC/1012 and P-90 - industrial supercomputer database machines

Cariño, Felipe; Kostamaa, Pekka

doi:10.1007/3-540-55599-4_130

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…During the past few years, much research effort has focused on developing high-performance database management systems. One approach is to build multiprocessor database machines, which have become increasingly popular (for example, Bubba [4], Gamma [12], Teradata [5], [13], Tandem [25], Oracle parallel server [14], DB2 parallel edition [2]). In such systems, database relations are generally partitioned horizontally and distributed across multiple processors.…”

Section: Introductionmentioning

confidence: 99%

Scalability analysis of declustering methods for multidimensional range queries

Moon

Saltz

1998

IEEE Trans. Knowl. Data Eng.

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Efficient storage and retrieval of multiattribute data sets has become one of the essential requirements for many data-intensive applications. The Cartesian product file has been known as an effective multiattribute file structure for partial-match and best-match queries. Several heuristic methods have been developed to decluster Cartesian product files across multiple disks to obtain high performance for disk accesses. Although the scalability of the declustering methods becomes increasingly important for systems equipped with a large number of disks, no analytic studies have been done so far. In this paper, we derive formulas describing the scalability of two popular declustering methods¦Disk Modulo and Fieldwise Xor¦for range queries, which are the most common type of queries. These formulas disclose the limited scalability of the declustering methods, and this is corroborated by extensive simulation experiments. From the practical point of view, the formulas given in this paper provide a simple measure that can be used to predict the response time of a given range query and to guide the selection of a declustering method under various conditions.

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Section: Introductionmentioning

confidence: 99%

Scalability analysis of declustering methods for multidimensional range queries

Moon

Saltz

1998

IEEE Trans. Knowl. Data Eng.

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“…Editors of parallel servers offer three main categories of parallel RDBMS based on: (i) shared memory multiprocessor architecture, (ii) shared disks multiprocessor architecture, and (iii) shared nothing multiprocessor architecture (e.g. DBC 1012 Teradata [8,64], Tandem NonStop SQL [23], DB2 Edition [5], ORACLE Parallel Query).…”

Section: Parallel Relational Database Systemsmentioning

confidence: 99%

Big Data Management in the Cloud: Evolution or Crossroad?

Hameurlain

Morvan

2016

Communications in Computer and Information Science

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. In this paper, we try to provide a synthetic and comprehensive state of the art concerning big data management in cloud environments. In this perspective, data management based on parallel and cloud (e.g. MapReduce) systems are overviewed, and compared by relying on meeting software requirements (e.g. data independence, software reuse), high performance, scalability, elasticity, and data availability. With respect to proposed cloud systems, we discuss evolution of their data manipulation languages and we try to learn some lessons should be exploited to ensure the viability of the next generation of large-scale data management systems for big data applications.

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“…This section deals with the Data Warehouse, Data Mart and database architecture options for organizing and accessing large volumes of historic atomic and SQL LOB (Large Object) data. The key to successful Enterprise-wide DW is to store and mine all detail data and convert it into strategic information [Arm98] and [CK92]. The new enabling factor is cost-effective storage hence all detail data can be directly queried and/or used to fuel/load data into operational systems (e.g.…”

Section: Data Warehousingmentioning

confidence: 99%

StorHouse metanoia - new applications for database, storage & data warehousing

Cariño¹,

Kostamaa²,

Kaufmann³

et al. 2001

SIGMOD Rec.

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This paper describes the StorHouse/Relational Manager (RM) database system that uses and exploits an active storage hierarchy. By active storage hierarchy, we mean that StorHouse/RM executes SQL queries directly against data stored on all hierarchical storage (i.e. disk, optical, and tape) without post processing a file or a DBA having to manage a data set. We describe and analyze StorHouse/RM features and internals. We also describe how StorHouse/RM differs from traditional HSM (Hierarchical Storage Management) systems. For commercial applications we describe an evolution to the Data Warehouse concept, called Atomic Data Store, whereby atomic data is stored in the database system. Atomic data is defined as storing all the historic data values and executing queries against them. We also describe a Hub-and-Spoke Data Warehouse architecture, which is used to feed or fuel data into Data Marts.Furthermore, we provide analysis how StorHouse/RM can be federated with DB2, Oracle and Microsoft SQL Server 7 (SS7) and thus provide these databases an active storage hierarchy (i.e. tape). We then show two federated data modeling techniques (a) logical horizontal partitioning (LHP) of tuples and (b) logical vertical partitioning (LVP) of columns to demonstrate our database extension capabilities.We conclude with a TPC-like performance analysis of data stored on tape and disk.

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Exegesis of DBC/1012 and P-90 - industrial supercomputer database machines

Cited by 12 publications

References 15 publications

Scalability analysis of declustering methods for multidimensional range queries

Scalability analysis of declustering methods for multidimensional range queries

Big Data Management in the Cloud: Evolution or Crossroad?

StorHouse metanoia - new applications for database, storage & data warehousing

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