Monet and its geographical extensions: A novel approach to high performance GIS processing

Boncz, Peter; Quak, Wilko; Kersten, Martin

doi:10.1007/bfb0014150

Cited by 22 publications

(15 citation statements)

References 12 publications

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“…Among them, Kx Systems can be regarded as an array database because it treats an array as a first-class citizen like an integer number. There are a number of research systems that uses vertical data organization as well, for example, C-Store [91,90], MonetDB [17,16], and FastBit. One common feature of all these systems is that they logically organize values of a column together.…”

Section: Data Processing Systemsmentioning

confidence: 99%

Accelerating Queries on Very Large Datasets

Otoo¹,

Wu²

2009

Scientific Data Management

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In this chapter, we explore ways to answer queries on large multi-dimensional data efficiently. Given a large dataset, a user often wants to access only a relatively small number of the records. Such a selection process is typically performed through an SQL query in a database management system (DBMS). In general, the most effective technique to accelerate the query answering process is indexing. For this reason, our primary emphasis is to review indexing techniques for large datasets. Since much of scientific data is not under the management of DBMS systems, our review includes many indexing techniques outside of DBMS systems as well. Among the known indexing methods, bitmap indexes are particularly well suited for answering such queries on large scientific data. Therefore, more details are given on the state of the art of bitmap indexing techniques. This chapter also briefly touches on some emerging data analysis systems that don't yet make use of indexes. We present some evidence that these systems could also benefit from the use of indexes.

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Section: Data Processing Systemsmentioning

confidence: 99%

Accelerating Queries on Very Large Datasets

Otoo¹,

Wu²

2009

Scientific Data Management

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“…The underlying database used here is a relational DBMS. 5 Besides the operations and structures described in this section, others are available in the Moa system, e.g., the nest and unnest operations. However, due to space limitations, it is infeasible to present an exhaustive list.…”

Section: Fig 7 Screendump Of Moa Systemmentioning

confidence: 99%

“…The ideas for Moa and multi-model architecture originate from the Magnum-project [6,5]. In this project, a structurally object-oriented DBMS was developed for the purpose of efficiently integrating spatial and thematic data in a single data manager.…”

Section: Application Areas and Contextmentioning

confidence: 99%

Moa and the Multi-model Architecture: A New Perspective on NF2

Keulen

Vonk

Vries

et al. 2003

Lecture Notes in Computer Science

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Abstract. Advanced non-traditional application domains such as geographic information systems and digital library systems demand advanced data management support. In an effort to cope with this demand, we present the concept of a novel multi-model DBMS architecture which provides evaluation of queries on complexly structured data without sacrificing efficiency. A vital role in this architecture is played by the Moa language featuring a nested relational data model based on XNF 2 , in which we placed renewed interest. Furthermore, extensibility in Moa avoids optimization obstacles due to black-box treatment of ADTs. The combination of a mapping of queries on complexly structured data to an efficient physical algebra expression via a nested relational algebra, extensibility open to optimization, and the consequently better integration of domain-specific algorithms, makes that the Moa system can efficiently handle complex queries from non-traditional application domains.

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“…Our research group has studied large main-memory database systems for the past 10 years. This research started in the PRISMA project [AvdBF + 92], focusing on massive parallelism, and is now centered around Monet [BQK96,BWK98], a high-performance system targeted to query-intensive application areas like OLAP and Data Mining. For the research presented here, we use Monet as our experimentation platform.…”

Section: Overviewmentioning

confidence: 99%

Optimizing database architecture for the new bottleneck: memory access

Boncz²,

2000

Self Cite

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Abstract. In the past decade, advances in the speed of commodity CPUs have far out-paced advances in memory latency. Main-memory access is therefore increasingly a performance bottleneck for many computer applications, including database systems. In this article, we use a simple scan test to show the severe impact of this bottleneck. The insights gained are translated into guidelines for database architecture, in terms of both data structures and algorithms. We discuss how vertically fragmented data structures optimize cache performance on sequential data access. We then focus on equijoin, typically a random-access operation, and introduce radix algorithms for partitioned hash-join. The performance of these algorithms is quantified using a detailed analytical model that incorporates memory access cost. Experiments that validate this model were performed on the Monet database system. We obtained exact statistics on events such as TLB misses and L1 and L2 cache misses by using hardware performance counters found in modern CPUs. Using our cost model, we show how the carefully tuned memory access pattern of our radix algorithms makes them perform well, which is confirmed by experimental results.

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Monet and its geographical extensions: A novel approach to high performance GIS processing

Cited by 22 publications

References 12 publications

Accelerating Queries on Very Large Datasets

Accelerating Queries on Very Large Datasets

Moa and the Multi-model Architecture: A New Perspective on NF2

Optimizing database architecture for the new bottleneck: memory access

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