LARS: A Logic-based framework for Analytic Reasoning over Streams

Beck, Harald; Dao-Tran, Minh; Eiter, Thomas

doi:10.1016/j.artint.2018.04.003

Cited by 80 publications

(137 citation statements)

References 36 publications

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“…Unlike DyKnow, however, LARS does not consider the production of state streams. Key contributions presented as part of the LARS framework are reported (Beck et al, 2015) to include 1. a rule-based formalism for reasoning over streams; 2. different means to refer to or abstract from time; and 3. a window operator to this effect.…”

Section: Larsmentioning

confidence: 99%

“…The use of windows for stream reasoning has been studied by Beck et al (2014Beck et al ( , 2015 as part of a logic-based formalisation of stream reasoning. One result is the logical window operator , which describes the semantics of a large variety of windowing operations that can be applied to streams.…”

Section: Dsm Systemsmentioning

confidence: 99%

“…LARS is a Logic-based framework for Analysing Reasoning over Streams by Beck et al (2014Beck et al ( , 2015 and provides a logical formalisation of stream reasoning. LARS considers stream reasoning to be logical reasoning on streaming data, and therefore takes an approach wherein streaming data is modelled logically, i.e.…”

Section: Larsmentioning

confidence: 99%

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Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Leng¹

2017

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A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on wellknown database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system-by combining reasoning over and reasoning about streams-can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes.

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Section: Larsmentioning

confidence: 99%

Section: Dsm Systemsmentioning

confidence: 99%

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Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Leng¹

2017

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“…The W3C RSP community group adopts it, together with LARS [32], as a reference model to define the semantics of RSP query language.…”

Section: Modeling Stream Processingmentioning

confidence: 99%

Defining the execution semantics of stream processing engines

et al. 2017

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IntroductionSeveral modern data-intensive applications need to process large volumes of data on the fly as they are produced. Examples range from credit card fraud detection systems, which analyze massive streams of credit card transactions to identify suspicious patterns, to environmental monitoring applications that continuously analyze sensor data, to click stream analysis of Web sites that identify frequent patterns of interactions. More in general, stream processing is a central requirement in today's information systems.This state of facts pushed the development of several stream processing engines (SPEs) that continuously analyze streams of data to produce new results as new elements enter the streams. Unfortunately, existing SPEs adopt different processing models and standardized execution semantics have not yet emerged. This severely hampers the usability AbstractThe ability to process large volumes of data on the fly, as soon as they become available, is a fundamental requirement in today's information systems. Modern distributed stream processing engines (SPEs) address this requirement and provide low-latency and high-throughput data stream processing in cluster platforms, offering high-level programming interfaces that abstract from low-level details such as data distribution and hardware failures. The last decade saw a rapid increase in the number of available SPEs. However, each SPE defines its own processing model and standardized execution semantics have not emerged yet. This paper tackles this problem and analyzes the execution semantics of some widely adopted modern SPEs, namely Flink, Storm, Spark Streaming, Google Dataflow, and Azure Stream Analytics. We specifically target the notions of windowing and time, traditionally considered the key distinguishing factors that characterize the behavior of SPEs. We rely on the SECRET model, introduced in 2010 to analyze the windowing semantics for the SPEs available at that time. We show that SECRET models well some aspects of the behavior of modern SPEs, and we shed light on the evolution of SPEs after the introduction of SECRET by analyzing the elements that SECRET cannot fully capture. In this way, the paper contributes to the research in the area of stream processing by: (1) contrasting and comparing some widely used modern SPEs based on a formal model of their execution semantics; (2) discussing the evolution of SPEs since the introduction of the SECRET model; (3) suggesting promising research directions to direct further modeling efforts. Affetti et al. J Big Data (2017) et al. J Big Data (2017) 4:12 and interoperability of SPEs, since a user needs to understand system-specific aspects to confront various alternatives and select the ones that better suite her needs. SURVEY PAPERPage 2 of 24 AffettiThe main factors that differentiate the behaviors of SPEs are the models of windows and time they adopt [1]. Windows enable computations that would be otherwise unfeasible on unbounded datasets such as streams. For instance, counting the number o...

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“…Every RSP engine has unique features that are not replicable by others; moreover, even when the same feature is supported by two or more engines, the behavior and the produced output can be different and hardly comparable. In our previous work, namely RSP-QL [14] and LARS [7], we developed models to capture the RSP features inspired by the DSMS paradigm, e.g., time-based sliding windows and aggregations over streams.…”

Section: Introductionmentioning

confidence: 99%

A Query Model to Capture Event Pattern Matching in RDF Stream Processing Query Languages

Dell’Aglio

Dao-Tran

Calbimonte

et al. 2016

Lecture Notes in Computer Science

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Abstract. The current state of the art in RDF Stream Processing (RSP) proposes several models and implementations to combine Semantic Web technologies with Data Stream Management System (DSMS) operators like windows. Meanwhile, only a few solutions combine Semantic Web and Complex Event Processing (CEP), which includes relevant features, such as identifying sequences of events in streams. Current RSP query languages that support CEP features have several limitations: EP-SPARQL can identify sequences, but its selection and consumption policies are not all formally defined, while C-SPARQL offers only a naive support to pattern detection through a timestamp function. In this work, we introduce an RSP query language, called RSEP-QL, which supports both DSMS and CEP operators, with a special interest in formalizing CEP selection and consumption policies. We show that RSEP-QL captures EP-SPARQL and C-SPARQL, and offers features going beyond the ones provided by current RSP query languages.

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LARS: A Logic-based framework for Analytic Reasoning over Streams

Cited by 80 publications

References 36 publications

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Defining the execution semantics of stream processing engines

A Query Model to Capture Event Pattern Matching in RDF Stream Processing Query Languages

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