Scientific formats for object-relational database systems

Cohen, Shirley; Hurley, Patrick; Schulz, Karl; Barth, William L.; Benton, Brad

doi:10.1145/1147376.1147378

Cited by 18 publications

(8 citation statements)

References 1 publication

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“…Cohen et al use meteorological data to compare the efficiency of using a vector data type called VArray and Oracle nested tables against three different scientific purpose data containers: netCDF, HDF4, and HDF5 [21]. They show that HDF5 is the most efficient storage mechanism for large data volumes of meteorological data.…”

Section: Related Workmentioning

confidence: 99%

Optimization of PACS Data Persistency Using Indexed Hierarchical Data

Prado¹,

Macedo

Dantas³

et al. 2014

J Digit Imaging

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We present a new approach for the development of a data persistency layer for a Digital Imaging and Communications in Medicine (DICOM)-compliant Picture Archiving and Communications Systems employing a hierarchical database. Our approach makes use of the HDF5 hierarchical data storage standard for scientific data and overcomes limitations of hierarchical databases employing inverted indexing for secondary key management and for efficient and flexible access to data through secondary keys. This inverted indexing is achieved through a general purpose document indexing tool called Lucene. This approach was implemented and tested using real-world data against a traditional solution employing a relational database, in various store, search, and retrieval experiments performed repeatedly with different sizes of DICOM datasets. Results show that our approach outperforms the traditional solution on most of the situations, being more than 600 % faster in some cases.

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Section: Related Workmentioning

confidence: 99%

Optimization of PACS Data Persistency Using Indexed Hierarchical Data

Prado¹,

Macedo

Dantas³

et al. 2014

J Digit Imaging

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“…An experimental study 5 showed that, even if using array primitives in RDBMSs, native file formats outperformed the relational implementation by a factor of 20 to as much as 80. Proposed scientific DBMSs 6,23 provide multidimensional arrays as first-class types, aiming to bridge the gap between DBMSs and native files in the process.…”

Section: Data and Process Integrationmentioning

confidence: 99%

Managing scientific data

Ailamaki

2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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68 communications of th e ac m | j u n e 2 0 1 0 | vo l . 5 3 | n o. 6 contributed articles DATA -orienTeD sC i e nT i f iC P ro C es se s depend on fast, accurate analysis of experimental data generated through empirical observation and simulation. However, scientists are increasingly overwhelmed by the volume of data produced by their own experiments. With improving instrument precision and the complexity of the simulated models, data overload promises to only get worse. The inefficiency of existing database management systems (DBMSs) for addressing the requirements of scientists has led to many application-specific systems. Unlike their general-purpose counterparts, these systems require more resources, hindering reuse of knowledge. Still, the data-management community aspires to generalpurpose scientific data management. Here, we explore the most important requirements of such systems and the techniques being used to address them.Observation and simulation of phenomena are keys for proving scientific theories and discovering facts of nature the human brain could otherwise never imagine. Scientists must be able to manage data derived from observations and simulations. Constant improvement of observational instruments and simulation tools give modern science effective options for abundant information capture, reflecting the rich diversity of complex life forms and cosmic phenomena. Moreover, the need for in-depth analysis of huge amounts of data relentlessly drives demand for additional computational support.Microsoft researcher and ACM Turing Award laureate Jim Gray once said, "A fourth data-intensive science is emerging. The goal is to have a world in which all of the science literature is online, all the science data is online, and they interoperate with each other."9 Unfortunately, today's commercial data-management tools are incapable of supporting the unprecedented scale, rate, and complexity of scientific data collection and processing.Despite its variety, scientific data does share some common features: Needed are generic, rather than one-off, DBMS solutions automating storage and analysis of data from scientific collaborations.BY anastasia aiLamaKi, VeRena KanteRe, anD DeBaBRata Dash managing scientific Data key insights managing the enormous amount of scientific data being collected is the key to scientific progress. though technology allows for the extreme collection rates of scientific data, processing is still performed with stale techniques developed for small data sets; efficient processing is necessary to be able to exploit the value of huge scientific data collections.Proposed solutions also promise to achieve efficient management for almost any other kind of data. Lack of complete solutions using commercial DBMSs has led scientists in all fields to develop or adopt application-specific solutions, though some have been added on top of commercial DBMSs; for example, the Sloan Digital Sky Survey (SDSS-1 and SDSS-2; http://www.sdss.org/) uses SQL Server as its backend. Moreover, the resulting sof...

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“…Relational database management systems (RDBMSs) have repeatedly shown that they are very efficient, scalable and successful in hosting types of data which have formerly not been anticipated to be stored inside relational databases such as complex objects [21] , spatio-temporal data [22] and XML data [23] . In addition, RDBMSs have shown its ability to handle vast amounts of data very efficiently using its powerful indexing mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Efficient Relational Techniques for Processing Graph Queries

Sakr

Al‐Naymat

2010

J. Comput. Sci. Technol.

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Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions and semantic web. To effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. For example, Subgraph and Supergraph queries are important types of graph queries which have many applications in practice. A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems. Relational database management systems (RDBMSs) have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data. RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness, proper join ordering and powerful indexing mechanisms. In this article, we study the problem of indexing and querying graph databases using the relational infrastructure. We present a purely relational framework for processing graph queries. This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database. We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries. Finally, we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.

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Scientific formats for object-relational database systems

Cited by 18 publications

References 1 publication

Optimization of PACS Data Persistency Using Indexed Hierarchical Data

Optimization of PACS Data Persistency Using Indexed Hierarchical Data

Managing scientific data

Efficient Relational Techniques for Processing Graph Queries

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