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  • André-Jönsson, Henrik, 1968- (författare)
  • Indexing strategies for time series data
  • 2002
  • Doktorsavhandling (övrigt vetenskapligt)abstract
    • <p>Traditionally, databases have stored textual data and have been used to store administrative information. The computers used. and more specifically the storage available, have been neither large enough nor fast enough to allow databases to be used for more technical applications. In recent years these two bottlenecks have started to di sappear and there is an increasing interest in using databases to store non-textual data like sensor measurements or other types of process-related data. In a database a sequence of sensor measurements can be represented as a time series. The database can then be queried to find, for instance, subsequences, extrema points, or the points in time at which the time series had a specific value. To make this search efficient, indexing methods are required. Finding appropriate indexing methods is the focus of this thesis.</p><p>There are two major problems with existing time series indexing strategies: the size of the index structures and the lack of general indexing strategies that are application independent. These problems have been thoroughly researched and solved in the case of text indexing files. We have examined the extent to which text indexing methods can be used for indexing time series.</p><p>A method for transforming time series into text sequences has been investigated. An investigation was then made on how text indexing methods can be applied on these text sequences. We have examined two well known text indexing methods: the signature files and the B-tree. A study has been made on how these methods can be modified so that they can be used to index time series. We have also developed two new index structures, the signature tree and paged trie structures. For each index structure we have constructed cost and size models. resulting in comparisons between the different approaches.</p><p>Our tests indicate that the indexing method we have developed. together with the B-tree structure. produces good results. It is possible to search for and find sub-sequences of very large time series efficiently.</p><p>The thesis also discusses what future issues will have to be investigated for these techniques to be usable in a control system relying on time-series indexing to identify control modes.</p>
  • Andrejev, Andrej, et al. (författare)
  • Scientific data as RDF with arrays : Tight integration of SciSPARQL queries into MATLAB
  • 2014
  • Ingår i: SSPI. - RWTH Aachen University.
  • Konferensbidrag (refereegranskat)abstract
    • <p>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.</p>
  • Andrejev, Andrej, 1980- (författare)
  • Semantic Web Queries over Scientific Data
  • 2016
  • Doktorsavhandling (övrigt vetenskapligt)abstract
    • <p>Semantic Web and Linked Open Data provide a potential platform for interoperability of scientific data, offering a flexible model for providing machine-readable and queryable metadata. However, RDF and SPARQL gained limited adoption within the scientific community, mainly due to the lack of support for managing massive numeric data, along with certain other important features – such as extensibility with user-defined functions, query modularity, and integration with existing environments and workflows.</p><p>We present the design, implementation and evaluation of <em>Scientific SPARQL</em> – a language for querying data and metadata combined, represented using the RDF graph model extended with numeric multidimensional arrays as node values – <em>RDF with Arrays</em>. The techniques used to store <em>RDF with Arrays</em> in a scalable way and process Scientific SPARQL queries and updates are implemented in our prototype software – <em>Scientific SPARQL Database Manager, SSDM</em>, and its integrations with data storage systems and computational frameworks. This includes scalable storage solutions for numeric multidimensional arrays and an efficient implementation of array operations. The arrays can be physically stored in a variety of external storage systems, including files, relational databases, and specialized array data stores, using our <em>Array Storage Extensibility Interface</em>. Whenever possible SSDM accumulates array operations and accesses array contents in a lazy fashion.</p><p>In scientific applications numeric computations are often used for filtering or post-processing the retrieved data, which can be expressed in a functional way. Scientific SPARQL allows expressing common query sub-tasks with functions defined as parameterized queries. This becomes especially useful along with functional language abstractions such as <em>lexical closures</em> and <em>second-order functions</em>, e.g. array mappers.</p><p>Existing computational libraries can be interfaced and invoked from Scientific SPARQL queries as <em>foreign functions</em>. Cost estimates and alternative evaluation directions may be specified, aiding the construction of better execution plans. Costly array processing, e.g. filtering and aggregation, is thus preformed on the server, saving the amount of communication. Furthermore, common supported operations are delegated to the array storage back-ends, according to their capabilities. Both expressivity and performance of Scientific SPARQL are evaluated on a real-world example, and further performance tests are run using our mini-benchmark for array queries.</p>
  • Andrejev, Andrej, et al. (författare)
  • Spatio-Temporal Gridded Data Processing on the Semantic Web
  • 2015
  • Ingår i: 2015 IEEE International Conference On Data Science And Data Intensive Systems. - 9781509002146 ; s. 38-45
  • Konferensbidrag (refereegranskat)abstract
    • <p>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.</p>
  • Badiozamany, Sobhan, 1983-, et al. (författare)
  • Framework for real-time clustering over sliding windows
  • 2016
  • Ingår i: Proc. 28th International Conference on Scientific and Statistical Database Management. - New York : ACM Press. - 978-1-4503-4215-5 ; s. 1-13
  • Konferensbidrag (refereegranskat)
  • Badiozamany, Sobhan, 1983- (författare)
  • Real-time data stream clustering over sliding windows
  • 2016
  • Doktorsavhandling (övrigt vetenskapligt)abstract
    • <p>In many applications, e.g. urban traffic monitoring, stock trading, and industrial sensor data monitoring, clustering algorithms are applied on data streams in real-time to find current patterns. Here, sliding windows are commonly used as they capture concept drift.</p><p>Real-time clustering over sliding windows is early detection of continuously evolving clusters as soon as they occur in the stream, which requires efficient maintenance of cluster memberships that change as windows slide.</p><p>Data stream management systems (DSMSs) provide high-level query languages for searching and analyzing streaming data. In this thesis we extend a DSMS with a real-time data stream clustering framework called <em>Generic 2-phase Continuous Summarization framework (G2CS)</em>.  G2CS modularizes data stream clustering by taking as input clustering algorithms which are expressed in terms of a number of functions and indexing structures. G2CS supports real-time clustering by efficient window sliding mechanism and algorithm transparent indexing. A particular challenge for real-time detection of a high number of rapidly evolving clusters is efficiency of window slides for clustering algorithms where deletion of expired data is not supported, e.g. BIRCH. To that end, G2CS includes a novel window maintenance mechanism called Sliding Binary Merge (SBM). To further improve real-time sliding performance, G2CS uses generation-based multi-dimensional indexing where indexing structures suitable for the clustering algorithms can be plugged-in.</p>
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