| 1. |
- Andrejev, Andrej, et al.
(author)
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Spatio-Temporal Gridded Data Processing on the Semantic Web
- 2015
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In: 2015 IEEE International Conference On Data Science And Data Intensive Systems. - 9781509002146 ; , s. 38-45
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Conference paper (peer-reviewed)abstract
- Multidimensional array data, such as remote-sensing imagery and timeseries, climate model simulations, telescope observations, and medical images, contribute massively to virtually all science and engineering domains, and hence play a key role in 'Big Data' challenges. Pure array storage management and analytics is relatively well understood today. However, arrays in practice never come alone, but are accompanied by metadata, including domain, range, provenance information, etc. The structure of this metadata is far less regular than arrays or tables, and may be incomplete or different from one array instance to another. Particularly in the field of the Semantic Web such integrated representations must convey a sufficiently complete and reasonable semantics for machine-machine communication. We show how the Resource Description Framework (RDF), the Semantic Web graph model for metadata, can be leveraged for such data/metadata integration specifically for representing spatio-temporal grid data. Based on the notion of a coverage as established by the Open Geospatial Consortium (OGC) we present a hybrid data store where efficiently represented arrays are incorporated as nodes into RDF graphs and connected to their metadata. We have extended the Semantic Web query language SPARQL to incorporate array query semantics and other functionality making it suitable for processing of large numeric arrays, including geo coverages.
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| 2. |
- Mahmood, Khalid, et al.
(author)
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Utilizing a NoSQL Data Store for Scalable Log Analysis
- 2015
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Conference paper (peer-reviewed)abstract
- A potential problem for persisting large volume of data logs with a conventional relational database is that loading massive logs produced at high rates is not fast enough due to the strong consistency model and high cost of indexing. As a possible alternative, a modern NoSQL data store, which sacrifices transactional consistency to achieve higher performance and scalability, can be utilized. In this paper, we investigate to what degree a state-of-the-art NoSQL database can achieve high performance persisting and fundamental analyses of large-scale data logs from real world applications. For the evaluation, a state-of-the-art NoSQL database, MongoDB, is compared with a relational DBMS from a major commercial vendor and with a popular open source relational DBMS. MongoDB is chosen as it provides both primary and secondary indexing compared to other popular NoSQL systems. These indexing techniques are essential for scalable processing of queries over large scale data logs. To explore the impact of parallelism on query execution, sharding was investigated for MongoDB. Our results revealed that relaxing the consistency did not provide substantial performance enhancement in persisting large-scale data logs for any of the systems. However, for high-performance loading and analysis of data logs, MongoDB is shown to be a viable alternative compared to relational databases for queries where the choice of an optimal execution plan is not critical.
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| 3. |
- Truong, Thanh, et al.
(author)
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Scalable Numerical Queries by Algebraic Inequality Transformations
- 2014
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In: Database Systems for Advanced Applications, Dasfaa 2014, PT I. - 9783319058108 - 9783319058092 ; , s. 95-109
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Conference paper (peer-reviewed)abstract
- To enable historical analyses of logged data streams by SQL queries, the Stream Log Analysis System (SLAS) bulk loads data streams derived from sensor readings into a relational database system. SQL queries over such log data often involve numerical conditions containing inequalities, e. g. to find suspected deviations from normal behavior based on some function over measured sensor values. However, such queries are often slow to execute, because the query optimizer is unable to utilize ordered indexed attributes inside numerical conditions. In order to speed up the queries they need to be reformulated to utilize available indexes. In SLAS the query transformation algorithm AQIT (Algebraic Query Inequality Transformation) automatically transforms SQL queries involving a class of algebraic inequalities into more scalable SQL queries utilizing ordered indexes. The experimental results show that the queries execute substantially faster by a commercial DBMS when AQIT has been applied to preprocess them.
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| 4. |
- Zhu, Minpeng, et al.
(author)
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Querying Combined Cloud-Based and Relational Databases
- 2011
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Conference paper (peer-reviewed)abstract
- An increasing amount of data is stored in cloud repositories, which provide high availability, accessibility, and scalability. However, for security reasons enterprises often need to store the core proprietary data in their own relational databases, while common data to be widely available can be stored in a cloud data repository. For example, the subsidiaries of a global enterprise are located in different geographic places where each subsidiary is likely to maintain its own local database. In such a scenario, data integration among the local databases and the cloud-based data is inevitable. We have developed a system called BigIntegrator to enable general queries that combine data in cloud-based data stores with relational databases. We present the design and working principle of the system. A scenario of querying data from both kinds of data sources is used as illustration. The system is general and extensible to integrate data from different kinds of data sources. A particular challenge being addressed is the limited query capabilities of cloud data stores. BigIntegrator utilizes knowledge of those limitations to produce efficient query execution.
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| 5. |
- Zhu, Minpeng, et al.
(author)
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Scalable Numerical SPARQL Queries over Relational Databases
- 2014
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Conference paper (peer-reviewed)abstract
- We present an approach for scalable processing of SPARQL queries to RDF views of numerical data stored in relational databases (RDBs). Such queries include numerical expressions, inequalities, comparisons, etc. inside FILTERs. We call such FILTERs numerical expressions and the queries - numerical SPARQL queries. For scalable execution of numerical SPARQL queries over RDBs, numerical operators should be pushed into SQL rather than executing the filters as post-processing outside the RDB; otherwise the query execution is slowed down, since a lot of data is transported from the RDB server and furthermore indexes on the server are not utilized. The NUMTranslator algorithm converts numerical expressions in numerical SPARQL queries into corresponding SQL expressions. We show that NUMTranslator improves substantially the scalability of SPARQL queries based on a benchmark that analyses numerical logs stored in an RDB. We compared the performance of our approach with the performance of other systems processing SPARQL queries to RDF views of RDBs and show that NUMTranslator improves substantially the scalability of numerical queries compared to the other systems’ approaches.
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| 6. |
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| 7. |
- Andrejev, Andrej, et al.
(author)
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Scientific data as RDF with arrays : Tight integration of SciSPARQL queries into MATLAB
- 2014
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In: Proc. ISWC 2014 Posters & Demonstrations Track. - : RWTH Aachen University. ; , s. 221-224
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Conference paper (peer-reviewed)abstract
- We present an integrated solution for storing and querying scientific data and metadata, using MATLAB envi ronment as client front-end and our prototype DBMS on the server. We use RDF for experiment metadata, and numeric arrays for the rest. Our extension of SPARQL supports array operations and extensibility with foreign functions.
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| 8. |
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| 9. |
- Badiozamany, Sobhan, 1983-, et al.
(author)
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Framework for real-time clustering over sliding windows
- 2016
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In: Proc. 28th International Conference on Scientific and Statistical Database Management. - New York : ACM Press. - 9781450342155 ; , s. 1-13
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Conference paper (peer-reviewed)
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| 10. |
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