TPStream: low-latency and high-throughput temporal pattern matching on event streams

Körber, Michael; Glombiewski, Nikolaus; Morgen, Andreas; Seeger, Bernhard

doi:10.1007/s10619-019-07272-z

Cited by 12 publications

(11 citation statements)

References 39 publications

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“…There is a renewed interest in employing Allen's interval relations in different areas, e.g., for describing complex temporal events in event detection frameworks [20,27,28] as well as for querying temporal relationships in knowledge graphs via SPARQL [11]. One reason is that it is more natural for humans to work with chunks of information, such as labeled intervals, rather than individual values [21].…”

Section: Related Workmentioning

confidence: 99%

“…Allen's relationships are not only used in temporal databases, but also in event detection systems for temporal pattern matching [20,27,28]. In this context, it is also important to be able to specify concrete time frames within which certain patterns are encountered, introducing the need for parameterized versions of Allen's relationships.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, it is also important to be able to specify concrete time frames within which certain patterns are encountered, introducing the need for parameterized versions of Allen's relationships. For instance, Körber et al use their TPStream framework for analyzing real-time traffic data [27,28], while we previously employed a language called ISEQL to specify events in a video surveillance context [20]. Event detection motivated us to develop an approach that is also applicable for event stream processing environments, meaning that our join operators are non-blocking and produce output tuples as early as logically possible, without necessarily waiting for the intervals to finish.…”

Section: Introductionmentioning

confidence: 99%

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Cache-efficient sweeping-based interval joins for extended Allen relation predicates

et al. 2021

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We develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cache-efficient sweeping-based interval joins for extended Allen relation predicates

et al. 2021

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“…In this context, it is also important to be able to specify concrete timeframes within which certain patterns are encountered, introducing the need for parameterized versions of Allen's relationships. For instance, Körber et al use their TPStream framework for analyzing real-time traffic data [27,28], while we previously employed a language called ISEQL to specify events in a video surveillance context [20]. Event detection motivated us to develop an approach that is also applicable for event stream processing environments, meaning that our join operators are non-blocking and produce output tuples as early as logically possible, without necessarily waiting for the intervals to finish.…”

Section: Introductionmentioning

confidence: 99%

Cache-Efficient Sweeping-Based Interval Joins for Extended Allen Relation Predicates (Extended Version)

Piatov,

Helmer,

Dignös

et al. 2020

Preprint

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We develop a family of efficient plane-sweeping interval join algorithms that can evaluate a wide range of interval predicates such as Allen's relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-ofthe-art techniques, while being much better suited for real-time event processing.

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“…Since the storage engine of Vat is not optimized for this kind of workload, we implemented a connection from Vat to Java Event Processing Connectivity (Jepc), a middleware for uniform event processing. As depicted in Figure 1, two of the core features in Jepc are the utilization of the dedicated event storage system ChronicleDB and native, specialized CEP operators for advanced temporal [12] and spatio-temporal pattern matching queries. To facilitate the interaction between Vat and Jepc, ChronicleDB serves multiple roles.…”

Section: General Architecturementioning

confidence: 99%

VAT to the Future: Extrapolating Visual Complex Event Processing

Beilschmidt¹,

Drönner²,

Glombiewski³

et al.

EPiC Series in Computing

Self Cite

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A key technology to analyze high volume spatio-temporal data streams is complex event processing (CEP). CEP is unique in its ability to not only continuously process data as it arrives through common operations such as aggregations, but also to support pattern matching queries. Pattern Matching allows to detect a user-defined sequence of temporal predicates on event streams. The high volume flight data as provided by the OpenSky Network has a lot of characteristics that make it a perfect match for CEP. In particular, pattern matching operators can be utilized to detect a plethora of movement (landing, starting, evasion) and group patterns (airplanes closing in on each other) in a timely manner. However, CEP queries can be complex in nature and may require a combination of domain expertise and historical data analysis in order to deliver the desired results. In order to address these issues, we have combined a database-backed CEP system (ChronicleDB) with a scientific toolbox for interactive data exploration and geo visualization (Vat System). This allows users to interactively execute CEP queries and visually confirm the validity of their results, thus, simplifying the parameter tuning considerably.In addition, our solution supports efficient and interactive time travel queries. It allows to combine event streams with additional data sources (e.g., remote sensing images) and processing technologies (e.g., machine learning models) to extract higher level knowledge. Finally, our ongoing work on visual analytics explores extrapolating query results to provide more timely feedback for critical situations and multi-query optimization techniques to allow for an even more efficient system in general.

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TPStream: low-latency and high-throughput temporal pattern matching on event streams

Cited by 12 publications

References 39 publications

Cache-efficient sweeping-based interval joins for extended Allen relation predicates

Cache-efficient sweeping-based interval joins for extended Allen relation predicates

Cache-Efficient Sweeping-Based Interval Joins for Extended Allen Relation Predicates (Extended Version)

VAT to the Future: Extrapolating Visual Complex Event Processing

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