Browsing by Author "Vansummeren, Stijn"

Now showing 1 - 3 of 3
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    A Formal Framework for Complex Event Recognition
    (2021) Grez, Alejandro; Riveros, Cristian; Ugarte, Martin; Vansummeren, Stijn
    Complex event recognition (CER) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real time. CER finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. Existing CER languages lack a clear semantics, however, which makes them hard to understand and generalize. Moreover, there are no general techniques for evaluating CER query languages with clear performance guarantees.
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    Efficient Query Processing for Dynamically Changing Datasets
    (2019) Idris, Muhammad; Ugarte, Martin; Vansummeren, Stijn; Voigt, Hannes; Lehner, Wolfgang
    The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. Traditional approaches to this problem were developed around the notion of Incremental View Maintenance (IVM), and are based either on the materialization of subresults (to avoid their recomputation) or on the recomputation of subresults (to avoid the space overhead of materialization). Both techniques are suboptimal: instead of materializing results and subresults, one may also maintain a data structure that supports efficient maintenance under updates and from which the full query result can quickly be enumerated. In two previous articles, we have presented algorithms for dynamically evaluating queries that are easy to implement, efficient, and can be naturally extended to evaluate queries from a wide range of application domains. In this paper, we discuss our algorithm and its complexity, explaining the main components behind its efficiency. Finally, we show experiments that compare our algorithm to a state-of-the-art (Higher-order) IVM engine, as well as to a prominent complex event recognition engine. Our approach outperforms the competitor systems by up to two orders of magnitude in processing time, and one order in memory consumption.
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    Representing Paths in Graph Database Pattern Matching
    (2023) Martens, Wim; Niewerth, Matthias; Popp, Tina; Rojas, Carlos; Vansummeren, Stijn; Vrgoc, Domagoj
    Modern graph database query languages such as GQL, SQL/PGQ, and their academic predecessor G-Core promote paths to first-class citizens in the sense that their pattern matching facility can return paths, as opposed to only nodes and edges. This is challenging for database engines, since graphs can have a large number of paths between a given node pair, which can cause huge intermediate results in query evaluation., We introduce the concept of path multiset representations (PMRs), which can represent multisets of paths exponentially succinctly and therefore bring significant advantages for representing intermediate results. We give a detailed theoretical analysis that shows that they are especially well-suited for representing results of regular path queries and extensions thereof involving counting, random sampling, and unions. Our experiments show that they drastically improve scalability for regular path query evaluation, with speedups of several orders of magnitude.