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Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data (events) remains a challenge–that is, how to integrate heterogeneous event streams, combine them with background knowledge (eg, an ontology), and perform event processing and stream reasoning.
Proceedings of the 20th …, 2011
Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams.
Lecture Notes in Computer Science, 2018
Many ICT applications need to make sense of large volumes of streaming data to detect situations of interest and enable timely reactions. The Stream Reasoning (SR) domain aims to combine the performance of stream/event processing and the reasoning expressiveness of knowledge representation systems by adopting Semantic Web standards to represent streaming elements. In this paper, we argue that the mainstream SR model is not flexible enough to properly express the temporal relations common in many applications. We show that the model can miss relevant information and lead to inconsistent derivations. Moving from these premises, we introduce a novel SR model that provides expressive ontological and temporal reasoning by neatly decoupling their scope to avoid information losses and inconsistencies. We implement the model in the DOTR system that defines ontological reasoning using Datalog rules and temporal reasoning using the TESLA Complex Event Processing language, which builds on metric temporal logic. We demonstrate the expressiveness of our model through various examples and benchmarks, and we show that DOTR outperforms state-of-the-art SR tools, processing data with millisecond latency.
Lecture Notes in Computer Science, 2013
Stream reasoning is an emerging research field focused on dynamic processing and continuous reasoning over huge volumes of streaming data. Finding the right trade-off between scalability and expressivity is a key challenge in this area. In this paper, we want to provide a baseline for exploring the applicability of complex reasoning to the Web of Data based on a solution that combines results and approaches from database research, stream processing, and nonmonotonic logic programming.
International Journal of Semantic Computing, 2017
As distributed IoT applications become larger and more complex, the pure processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new pieces of knowledge. And since many IoT applications require almost real-time reactivity to stimulus of the environment, such information inference process has to be performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time reasoning based on the concepts of Semantic Stream and Fact Stream, as a natural extension of Complex Event Processing (CEP) and RDF (graph-based knowledge model). The main advantages of our approach are that: (a) it considers time as a key relation between pieces of information; (b) the processing of streams can be implemented using CEP; (c) it is general enough to be applied to any Data Stream Management Syst...
2009
While reasoners are year after year scaling up in the classical, time invariant domain of ontological knowledge, reasoning upon rapidly changing information has been neglected or forgotten. On the contrary, processing of data streams has been largely investigated and specialized Stream Database Management Systems exist. In this paper, by coupling reasoners with powerful, reactive, throughput-efficient stream management systems, we introduce the concept of Stream Reasoning.
2012
As data proliferates at increasing rates, the need for real-time stream processing applications increases as well. In the same way that data stream management systems have emerged from the database community, there is now a similar concern in managing dynamic knowledge among the Semantic Web community. Unfortunately, early relevant approaches are to a large extent theoretical and do not present convincing evidence of their efficiency in real dynamic environments. In this paper, we present a framework for the effective, real-time processing of streaming data and we define and analyze in depth its key components. Our framework serves as a basis for the implementation of the SensorStream prototype, on which we run numerous performance and scalability measurements that outline its behaviour and demonstrate its suitability and scalability for solutions that require real-time information processing from distributed and heterogeneous data sources.
IEEE Intelligent Systems, 2000
person behind the computer? Which content on the news Web portal is attracting the most attention? Which navigation pattern would lead readers to other news related to that content? Do trends in medical records indicate any new disease spreading in a given part of the world? Where are all my friends meeting? Can we detect any intra-day correlation clusters among stock exchanges? What are the top 10 emerging topics under discussion in the blogosphere, and who is driving the discussions?
A Formal Framework for Complex Event Processing, 2019
Complex Event Processing (CEP) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real-time. CEP finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. However, existing CEP languages lack from a clear semantics, making them hard to understand and generalize. Moreover, there are no general techniques for evaluating CEP query languages with clear performance guarantees. In this paper we embark on the task of giving a rigorous and efficient framework to CEP. We propose a formal language for specifying complex events, called CEL, that contains the main features used in the literature and has a denotational and compositional semantics. We also formalize the so-called selection strategies, which had only been presented as by-design extensions to existing frameworks. With a well-defined semantics at hand, we discuss how to efficiently process complex events by evaluating CEL formulas with unary filters. We start by studying the syntactical properties of CEL and propose rewriting optimization techniques for simplifying the evaluation of formulas. Then, we introduce a formal computational model for CEP, called complex event automata (CEA), and study how to compile CEL formulas with unary filters into CEA. Furthermore, we provide efficient algorithms for evaluating CEA over event streams using constant time per event followed by constant-delay enumeration of the results. Finally, we gather the main results of this work to present an efficient and declarative framework for CEP.
Expert Systems with Applications, 2015
The Live Web is characterised by a new way of interacting with the Web through dynamic streams of 28 relevant real-time contextual information to users. These sources of massive data usually overwhelm 29 them, because they are not able to consume that amount of data. Task Automation Services (TASs) are plat-30 forms or apps that allow their users to author automation rules to combine events from streams while 31 reducing the effort for handling incoming information. While these platforms are a reality, they suffer 32 from two major drawbacks: (i) the only incoming data streams available are those the TASs developers 33 decided to include in the system, and (ii) they lack of a mechanism to reason over large scale data outside 34 their platform. To face these challenges, this paper contributes in (i) reviewing the existing state of the art 35 including research and commercial work given their relevance. Based on the lessons learnt from this 36 review, (ii) we propose the Evented WEb ontology (EWE), that models the Evented WEb domain, and in 37 particular those concepts around TASs. EWE enables scalability, interoperability and definition of rules 38 with reasoning over Linked Open Data (LOD) cloud. To illustrate these contributions, (iii) a semantic 39 TAS has been implemented that benefits from the advantages EWE offers, and solves a realistic problem 40 using semantic technologies. Finally, (iv) to validate the ontology covers the domain it models, a thor-41 ough ontology evaluation is presented. 42
Abstract. In applications field such as business activity monitoring, telecommunications data management, web personalization and sensor networks event takes the form of continuous event streams. Clients require to sense situations and exceptions in these continuous event stream as opposed to one-time queries to a database. Our research is focused on the following real-time event processing issues: complex event analysis on event streams, QoS-aware event processing services, as well as a novel event stream processing model that ...
2011
Complex Event Processing (CEP) deals with processing of continuously arriving events with the goal of identifying meaningful patterns (complex events). In existing stream database approaches, CEP is manly concerned by temporal relations between events. This paper advocates for a knowledge-rich CEP with Stream Reasoning capabilities. Secondly, we address the problem of revision in event processing. Events are often assumed to be immutable and therefore always correct.
2017 IEEE 11th International Conference on Semantic Computing (ICSC), 2017
As distributed IoT applications become larger and more complex, the simple processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new bits of knowledge. And since many IoT applications require almost realtime reactivity to stimuli from the environment this information inference process has to be performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time symbolic reasoning based on the concepts of Semantic Stream and Fact Stream, as a natural extensions of Complex Event Processing (CEP) and RDF (graphbased knowledge model). The main advantages of our approach are that: (a) it considers time as a key relation between pieces of information; (b) the processing of streams can be implemented using CEP and that (c) it is general enough to be applied to any Data Stream Management System (DSMS). Index Terms-Internet of Things (IoT); sensors; data streams; complex event processing (CEP); semantic reasoning.
Semantic Web
The field of Complex Event Processing (CEP) relates to the techniques and tools developed to efficiently process pattern-based queries over data streams. The Semantic Web, through its standards and technologies, is in constant pursue to provide solutions for such paradigm while employing the RDF data model. The integration of Semantic Web technologies in this context can handle the heterogeneity, integration and interpretation of data streams at semantic level. In this paper, we propose and implement a new query language, called SPA , that extends SPARQL with new Semantic Complex Event Processing (SCEP) operators that can be evaluated over RDF graph-based events. The novelties of SPA includes (i) the separation of general graph pattern matching constructs and temporal operators; (ii) the support for RDF graph-based events and multiple RDF graph streams; and (iii) the expressibility of temporal operators such as Kleene+, conjunction, disjunction and event selection strategies; and (iv) the operators to integrate background information and streaming RDF graph streams. Hence, SPA enjoys good expressiveness compared with the existing solutions. Furthermore, we provide an efficient implementation of SPA using a non-deterministic automata (NFA) model for an efficient evaluation of the SPA queries. We provide the syntax and semantics of SPA and based on this, we show how it can be implemented in an efficient manner. Moreover, we also present an experimental evaluation of its performance, showing that it improves over state-of-the-art approaches.
2012 15th International Conference on Information Fusion, 2012
The main contribution of this paper is a practical semantic information integration approach for stream reasoning based on semantic matching. This is an important functionality for situation awareness applications where temporal reasoning over streams from distributed sources is needed. The integration is achieved by creating a common ontology, specifying the semantic content of streams relative to the ontology and then use semantic matching to find relevant streams. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning approach is integrated in the Robot Operating System (ROS) and used in collaborative unmanned aircraft systems missions.
ABSTRACT Complex Event Processing (CEP) is concerned with timely detection of complex events within a stream of atomic occurrences, and has useful applications in areas including financial services, mobile and sensor devices, click stream analysis etc. In this paper, we present an expressive formalism for specifying and combining complex events. For this language we provide both a clear declarative formal semantics as well as an effective event-driven execution model via a compilation strategy into Prolog.
Web Semantics: Science, Services and Agents on the World Wide Web, 2014
In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.
2009 3rd International Conference on New Technologies, Mobility and Security, 2009
One of the critical success factors of event-driven systems is the capability of detecting complex events from simple and ordinary event notifications. Complex events which trigger or terminate actionable situations can be inferred from large event clouds or event streams based on their event instance sequence, their syntax and semantics. Using semantics of event algebra patterns defined on top of event instance sequences for event detection is one of the promising approaches for detection of complex events. The developments and successes in building standards and tools for semantic technologies such as declarative rules and ontologies are opening novel research and application areas in event processing. One of these promising application areas is semantic event processing. In this paper we contribute with a conceptual approach which supports the implementation of the vision of semantic event-driven systems; using Semantic Web technologies, benefiting from complex event processing, and ensuring quality through trust and reputation management. All of these novel technologies leads to more intelligent decision supporting systems. 1
2010
A huge volume of Linked Data has been published on the Web, yet is not processable by Complex Event Processing (CEP) or Event Stream Processing (ESP) engines. This paper presents a frame-work to bridge this gap, under which Linked Data are first translated into events conforming to a lightweight ontology, and then fed to CEP engines. The event processing results will also be published back onto the Web of Data.
Lecture Notes in Computer Science, 2016
We outline the background, motivation, and requirements of an approach to create abstractions of event streams, which are timetagged sequences of events generated by an executing software system. Our work is motivated by the need to process event streams with millions of events that are generated by a spacecraft, that must be processed quickly after they are received on the ground. Our approach involves building a tool that adds hierarchical labels to a received event stream. The labels add contextual information to the event stream, and thus make it easier to build tools for visualizing and analyzing telemetry. We describe a notation for writing hierarchical labeling rules; the notation is based on a modification of Allen Logic, augmented with rule-definitions and features for referring to data in data parameterized events. We illustrate our notation and its use with an example.
2019
Data streams are increasingly needed for different types of applications and domains, where dynamicity and data velocity are of foremost importance. In this context, research challenges raise regarding the generation, publication, processing, and discovery of these streams, especially in distributed, heterogeneous and collaborative environments such as the Web. Stream reasoning has addressed some of these challenges in the last decade, presenting a novel data processing paradigm that lays at the intersection among semantic data modeling, stream processing, and inference techniques. However, stream reasoning works have focused almost exclusively on architectures and approaches that assume an isolated processing environment. Therefore, they lack, in general, the means for discovering, collaborating, negotiating, sharing, or validating data streams on a highly heterogeneous ecosystem as the Web. Agents and multi-agent systems research has long developed principles and foundations for enabling some of these features, although usually under assumptions that require to be revised in order to comply with the characteristics of data streams. This paper presents a vision for a Web of stream reasoning agents, capable of sharing not only streaming data, but also processing duties, using collaboration and negotiation protocols, while relying on common vocabularies and protocols that take into account the high dynamicity of their knowledge, goals, and behavioral patterns. CCS CONCEPTS • Information systems → Data streams; • Theory of computation → Semantics and reasoning; • Computing methodologies → Multi-agent systems;
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