Relevant bibliographies by topics / Predictive Complex Event Processing

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Predictive Complex Event Processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Predictive Complex Event Processing"

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Jia, Yunsong, Shuaiqi Huang, and Xiang Li. "Complex event processing system for IoT greenhouse." E3S Web of Conferences 267 (2021): 01048. http://dx.doi.org/10.1051/e3sconf/202126701048.

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Greenhouse is an important part of facility agriculture and a typical application scenario of modern agricultural technology. The greenhouse environment has the characteristics of nonlinearity, strong coupling, large inertia, and multiple disturbances. There are many environmental factors and it is a typical complex system [7]. In smart greenhouses, control commands are mostly triggered by complex events with multi-dimensional information. In this paper, by building the aggregation structure of complex events in the greenhouse, the technology is applied in the greenhouse as a whole. The core innovations of this paper are as follows: through the analysis of the information transmission process in the greenhouse, combined with the characteristics of the scene, a CEP information structure with predictive modules is formed, which is conducive to the popularization and application of CEP technology in the agricultural field. Pointed out the importance of extreme conditions in the prediction of the greenhouse environment for model evaluation. By improving the loss function in the machine learning algorithm, the prediction performance of a variety of algorithms under this condition has been improved. Applying CEP technology to intelligent greenhouse control scenarios, a set of practical complex event processing systems for greenhouse control has been formed.
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Wang, Yongheng, Hui Gao, and Guidan Chen. "Predictive complex event processing based on evolving Bayesian networks." Pattern Recognition Letters 105 (April 2018): 207–16. http://dx.doi.org/10.1016/j.patrec.2017.05.008.

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Nawaz, Falak, Naeem Khalid Janjua, and Omar Khadeer Hussain. "PERCEPTUS: Predictive complex event processing and reasoning for IoT-enabled supply chain." Knowledge-Based Systems 180 (September 2019): 133–46. http://dx.doi.org/10.1016/j.knosys.2019.05.024.

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Mdhaffar, Afef, Ismael Bouassida Rodriguez, Khalil Charfi, Leila Abid, and Bernd Freisleben. "CEP4HFP: Complex Event Processing for Heart Failure Prediction." IEEE Transactions on NanoBioscience 16, no. 8 (December 2017): 708–17. http://dx.doi.org/10.1109/tnb.2017.2769671.

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Zámečníková, Eva, and Jitka Kreslíková. "Performance Measurement of Complex Event Platforms." Journal of information and organizational sciences 40, no. 2 (December 9, 2016): 237–54. http://dx.doi.org/10.31341/jios.40.2.5.

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The aim of this paper is to find and compare existing solutions of complex event processing platforms (CEP). CEP platforms generally serve for processing and/or predicting of high frequency data. We intend to use CEP platform for processing of complex time series and integrate a solution for newly proposed method of decision making. The decision making process will be described by formal grammar. As there are lots of CEP solutions we will take the following characteristics under consideration - the processing in real time, possibility of processing of high volume data from multiple sources, platform independence, platform allowing integration with user solution and open license. At first we will talk about existing CEP tools and their specific way of use in praxis. Then we will mention the design of method for formalization of business rules used for decision making. Afterwards, we focus on two platforms which seem to be the best fit for integration of our solution and we will list the main pros and cons of each approach. Next part is devoted to benchmark platforms for CEP. Final part is devoted to experimental measurements of platform with integrated method for decision support.
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Terroso-Sáenz, Fernando, Jesús Cuenca-Jara, Aurora González-Vidal, and Antonio F. Skarmeta. "Human Mobility Prediction Based on Social Media with Complex Event Processing." International Journal of Distributed Sensor Networks 12, no. 9 (September 2016): 5836392. http://dx.doi.org/10.1177/155014775836392.

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Arwan, Achmad. "Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network." Register: Jurnal Ilmiah Teknologi Sistem Informasi 2, no. 2 (July 1, 2016): 79. http://dx.doi.org/10.26594/r.v2i2.550.

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AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman) yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats) that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results from training process), the system will issue a signal to increase production, otherwise system will keep monitor the events. Experiment result shows that the accuracy of this method is 77% for 39 series of event streams.Keywords: complex event processing, event, neural networks, process, production increase prediction.
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Fu, Bin Bin, and Jie Zhu. "A Research on Complex Event Processing Technology Based on Smart Logistic System." Applied Mechanics and Materials 722 (December 2014): 430–35. http://dx.doi.org/10.4028/www.scientific.net/amm.722.430.

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With IOT technology developing and the cost reducing, Its application in supply chain is a matter of time. Smart logistic system is one of the IOT technology application in supply chain which solve difficult problems, such as acquisition underlying data, information transfer and so on. we need to achieve higher level application and solve more complex problems such as improving inventory management accuracy, reducing supply chain management cost, improving accuracy of supply and demand prediction, supply chain's rapidly react ability,these need to use complex event processing technology. It will introduce how to apply complex event processing technology to supply chain system based on IOT. By this way we can sort out valuable information by processing a large number of simple event.
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Cannon, Jonathan. "Expectancy-based rhythmic entrainment as continuous Bayesian inference." PLOS Computational Biology 17, no. 6 (June 9, 2021): e1009025. http://dx.doi.org/10.1371/journal.pcbi.1009025.

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When presented with complex rhythmic auditory stimuli, humans are able to track underlying temporal structure (e.g., a “beat”), both covertly and with their movements. This capacity goes far beyond that of a simple entrained oscillator, drawing on contextual and enculturated timing expectations and adjusting rapidly to perturbations in event timing, phase, and tempo. Previous modeling work has described how entrainment to rhythms may be shaped by event timing expectations, but sheds little light on any underlying computational principles that could unify the phenomenon of expectation-based entrainment with other brain processes. Inspired by the predictive processing framework, we propose that the problem of rhythm tracking is naturally characterized as a problem of continuously estimating an underlying phase and tempo based on precise event times and their correspondence to timing expectations. We present two inference problems formalizing this insight: PIPPET (Phase Inference from Point Process Event Timing) and PATIPPET (Phase and Tempo Inference). Variational solutions to these inference problems resemble previous “Dynamic Attending” models of perceptual entrainment, but introduce new terms representing the dynamics of uncertainty and the influence of expectations in the absence of sensory events. These terms allow us to model multiple characteristics of covert and motor human rhythm tracking not addressed by other models, including sensitivity of error corrections to inter-event interval and perceived tempo changes induced by event omissions. We show that positing these novel influences in human entrainment yields a range of testable behavioral predictions. Guided by recent neurophysiological observations, we attempt to align the phase inference framework with a specific brain implementation. We also explore the potential of this normative framework to guide the interpretation of experimental data and serve as building blocks for even richer predictive processing and active inference models of timing.
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Wang, Yongheng, Xiaozan Zhang, and Zengwang Wang. "A Proactive Decision Support System for Online Event Streams." International Journal of Information Technology & Decision Making 17, no. 06 (November 2018): 1891–913. http://dx.doi.org/10.1142/s0219622018500463.

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In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision making model. A Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well for congestion control in transportation system.
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Dissertations / Theses on the topic "Predictive Complex Event Processing"

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Kammoun, Abderrahmen. "Enhancing Stream Processing and Complex Event Processing Systems." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES012.

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Alors que de plus en plus d'objets et d'appareils sensoriels connectés font partie de notre vie quotidienne, la masse d'informations circulant à grande vitesse ne cesse d'augmenter. Cette énorme quantité de données produites à des débits élevés exige une compréhension rapide pour être utile dans divers domaines d'activité telles que l'internet des objets, la santé, la gestion de l'énergie, etc. Les techniques traditionnelles de stockage et de traitement de données se sont révélées inefficaces ou inadaptables pour gérer ce flux de données. Cette thèse a pour objectif de proposer des solutions optimales à deux problèmes de recherche sur la gestion de flux de données. La première concerne l’optimisation de la résolution de requêtes continues complexes par les systèmes de détection d'événements complexes (CEP). La seconde aux problèmes liées à la prédiction des événement complexes fondée sur l’apprentissage de l’historique du système. Premièrement, nous avons proposé un modèle de recalcul pour le traitement de requêtes complexes, basé sur une indexation multidimensionnelle et des algorithmes de jointures optimisés. Deuxièmement, nous avons conçu un CEP prédictif qui utilise des informations historiques pour prédire des événements complexes futurs. Pour utiliser efficacement l'information historique, nous utilisons un espace de séquences historiques à N dimensions. Par conséquent, la prédiction peut être effectuée en répondant aux requêtes d’intervalles sur cet espace de séquences historiques. La pertinence des résultats obtenus, notamment par l'application de nos algorithmes et approches lors de challenges internationaux démontre la viabilité des méthodes que nous proposons
As more and more connected objects and sensory devices are becoming part of our daily lives, the sea of high-velocity information flow is growing. This massive amount of data produced at high rates requires rapid insight to be useful in various applications such as the Internet of Things, health care, energy management, etc. Traditional data storage and processing techniques are proven inefficient. This gives rise to Data Stream Management and Complex Event Processing (CEP) systems.This thesis aims to provide optimal solutions for complex and proactive queries. Our proposed techniques, in addition to CPU and memory efficiency, enhance the capabilities of existing CEP systems by adding predictive feature through real-time learning. The main contributions of this thesis are as follows:We proposed various techniques to reduce the CPU and memory requirements of expensive queries. These operators result in exponential complexity both in terms of CPU and memory. Our proposed recomputation and heuristic-based algorithm reduce the costs of these operators. These optimizations are based on enabling efficient multidimensional indexing using space-filling curves and by clustering events into batches to reduce the cost of pair-wise joins.We designed a novel predictive CEP system that employs historical information to predict future complex events. We proposed a compressed index structure, range query processing techniques and an approximate summarizing technique over the historical space.The applicability of our techniques over the real-world problems presented has produced further customize-able solutions that demonstrate the viability of our proposed methods
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Eckert, Michael. "Complex Event Processing with XChangeEQ." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-94051.

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Sazegarnejad, Mohammad Ali. "A model for complex event processing." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 2009. http://digitalcommons.auctr.edu/dissertations/1510.

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Advances in sensor technology will revolutionize the way that real-world events are collected and interpreted. The ability to ubiquitously capture data will generate an unprecedented amount of data making distributed data management and decision making key challenges in the deployment of this technology. The demands for intelligently managing real-time data and integrating it into applicable business processes have propelled the emergence of a new breed of distributed software systems. The challenges are broader than simply creating a software platform to manage and integrate the sheer volume of sensor data. Mechanisms that permit the application of contextual and application knowledge into the distributed decision making infrastructure are required. The design of such software is based on the theory of event which permits events to be states, or processes. In managing real-time data and information from distributed heterogeneous sensors, the notion of the event is attractive for several reasons. First, modeling data in terms of events parallels the way humans conceptualize and relate information. Second, the notion of events, especially the differentiation between significant and non-significant 1 events may be used to filter data. Third, the definition of an event provides an implicit data wrapper may be used to link sensor data through event relationships. These relationships may be used to reason in an enterprise application context. Finally, the event-based approach is well suited to associating autonomous, heterogeneous sensor nodes by means of the inherent properties of events such as time and space. Thus these sensor nodes may be integrated into a complex decision making networks through eventbased communication. In this thesis, the design and development of a distributed software platform which can acquire data from heterogeneous sensors, integrate, and provide distributed decision support is described. Raw data is processed at multiple levels of abstraction and using context infonnation combined to form higher-level events that enable real time decision making. A multi-layered event representation and reasoning model is implemented that feeds sensory data derived from low level sensors into higher-level event structures. Then, it can be exploited by appropriate event handlers. Alternate approaches to the “sense making” problem are discussed and the advantages of the proposed model is explained.
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Keskisärkkä, Robin. "Towards Semantically Enabled Complex Event Processing." Licentiate thesis, Linköpings universitet, Interaktiva och kognitiva system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141554.

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The Semantic Web provides a framework for semantically annotating data on the web, and the Resource Description Framework (RDF) supports the integration of structured data represented in heterogeneous formats. Traditionally, the Semantic Web has focused primarily on more or less static data, but information on the web today is becoming increasingly dynamic. RDF Stream Processing (RSP) systems address this issue by adding support for streaming data and continuous query processing. To some extent, RSP systems can be used to perform complex event processing (CEP), where meaningful high-level events are generated based on low-level events from multiple sources; however, there are several challenges with respect to using RSP in this context. Event models designed to represent static event information lack several features required for CEP, and are typically not well suited for stream reasoning. The dynamic nature of streaming data also greatly complicates the development and validation of RSP queries. Therefore, reusing queries that have been prepared ahead of time is important to be able to support real-time decision-making. Additionally, there are limitations in existing RSP implementations in terms of both scalability and expressiveness, where some features required in CEP are not supported by any of the current systems. The goal of this thesis work has been to address some of these challenges and the main contributions of the thesis are: (1) an event model ontology targeted at supporting CEP; (2) a model for representing parameterized RSP queries as reusable templates; and (3) an architecture that allows RSP systems to be integrated for use in CEP. The proposed event model tackles issues specifically related to event modeling in CEP that have not been sufficiently covered by other event models, includes support for event encapsulation and event payloads, and can easily be extended to fit specific use-cases. The model for representing RSP query templates was designed as an extension to SPIN, a vocabulary that supports modeling of SPARQL queries as RDF. The extended model supports the current version of the RSP Query Language (RSP-QL) developed by the RDF Stream Processing Community Group, along with some of the most popular RSP query languages. Finally, the proposed architecture views RSP queries as individual event processing agents in a more general CEP framework. Additional event processing components can be integrated to provide support for operations that are not supported in RSP, or to provide more efficient processing for specific tasks. We demonstrate the architecture in implementations for scenarios related to traffic-incident monitoring, criminal-activity monitoring, and electronic healthcare monitoring.
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Wang, Di. "Extending Complex Event Processing for Advanced Applications." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/235.

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Recently numerous emerging applications, ranging from on-line financial transactions, RFID based supply chain management, traffic monitoring to real-time object monitoring, generate high-volume event streams. To meet the needs of processing event data streams in real-time, Complex Event Processing technology (CEP) has been developed with the focus on detecting occurrences of particular composite patterns of events. By analyzing and constructing several real-world CEP applications, we found that CEP needs to be extended with advanced services beyond detecting pattern queries. We summarize these emerging needs in three orthogonal directions. First, for applications which require access to both streaming and stored data, we need to provide a clear semantics and efficient schedulers in the face of concurrent access and failures. Second, when a CEP system is deployed in a sensitive environment such as health care, we wish to mitigate possible privacy leaks. Third, when input events do not carry the identification of the object being monitored, we need to infer the probabilistic identification of events before feed them to a CEP engine. Therefore this dissertation discusses the construction of a framework for extending CEP to support these critical services. First, existing CEP technology is limited in its capability of reacting to opportunities and risks detected by pattern queries. We propose to tackle this unsolved problem by embedding active rule support within the CEP engine. The main challenge is to handle interactions between queries and reactions to queries in the high-volume stream execution. We hence introduce a novel stream-oriented transactional model along with a family of stream transaction scheduling algorithms that ensure the correctness of concurrent stream execution. And then we demonstrate the proposed technology by applying it to a real-world healthcare system and evaluate the stream transaction scheduling algorithms extensively using real-world workload. Second, we are the first to study the privacy implications of CEP systems. Specifically we consider how to suppress events on a stream to reduce the disclosure of sensitive patterns, while ensuring that nonsensitive patterns continue to be reported by the CEP engine. We formally define the problem of utility-maximizing event suppression for privacy preservation. We then design a suite of real-time solutions that eliminate private pattern matches while maximizing the overall utility. Our first solution optimally solves the problem at the event-type level. The second solution, at event-instance level, further optimizes the event-type level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our experimental evaluation over both real-world and synthetic event streams shows that our algorithms are effective in maximizing utility yet still efficient enough to offer near real time system responsiveness. Third, we observe that in many real-world object monitoring applications where the CEP technology is adopted, not all sensed events carry the identification of the object whose action they report on, so called €œnon-ID-ed€� events. Such non-ID-ed events prevent us from performing object-based analytics, such as tracking, alerting and pattern matching. We propose a probabilistic inference framework to tackle this problem by inferring the missing object identification associated with an event. Specifically, as a foundation we design a time-varying graphic model to capture correspondences between sensed events and objects. Upon this model, we elaborate how to adapt the state-of-the-art Forward-backward inference algorithm to continuously infer probabilistic identifications for non-ID-ed events. More important, we propose a suite of strategies for optimizing the performance of inference. Our experimental results, using large-volume streams of a real-world health care application, demonstrate the accuracy, efficiency, and scalability of the proposed technology.
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Qi, Yingmei. "High Performance Analytics in Complex Event Processing." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/2.

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Complex Event Processing (CEP) is the technical choice for high performance analytics in time-critical decision-making applications. Although current CEP systems support sequence pattern detection on continuous event streams, they do not support the computation of aggregated values over the matched sequences of a query pattern. Instead, aggregation is typically applied as a post processing step after CEP pattern detection, leading to an extremely inefficient solution for sequence aggregation. Meanwhile, the state-of-art aggregation techniques over traditional stream data are not directly applicable in the context of the sequence-semantics of CEP. In this paper, we propose an approach, called A-Seq, that successfully pushes the aggregation computation into the sequence pattern detection process. A-Seq succeeds to compute aggregation online by dynamically recording compact partial sequence aggregation without ever constructing the to-be-aggregated matched sequences. Techniques are devised to tackle all the key CEP- specific challenges for aggregation, including sliding window semantics, event purging, as well as sequence negation. For scalability, we further introduce the Chop-Connect methodology, that enables sequence aggregation sharing among queries with arbitrary substring relationships. Lastly, our cost-driven optimizer selects a shared execution plan for effectively processing a workload of CEP aggregation queries. Our experimental study using real data sets demonstrates over four orders of magnitude efficiency improvement for a wide range of tested scenarios of our proposed A-Seq approach compared to the state-of-art solutions, thus achieving high-performance CEP aggregation analytics.
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Zhang, Dazhi. "NEEL+: Supporting Predicates for Nested Complex Event Processing." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/991.

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"Complex event processing (CEP) has become increasingly important in modern applications, ranging from supply chain management for RFID tracking to real-time intrusion detection. These monitoring applications must detect complex event pattern sequences in event streams. However, the state-of-art in the CEP literature such as SASE, ZStream or Cayuga either do not support the specification of nesting for pattern queries altogether or they limit the nesting of non-occurrence expressions over composite event types. A recent work by Liu et al proposed a nested complex event pattern expression language, called NEEL (Nested Complex Event Language), that supports the specification of the non-occurrence over complex expressions. However, their work did not carefully consider predicate handling in these nested queries, especially in the context of complex negation. Yet it is well-known that predicate specification is a critical component of any query language. To overcome this gap, we now design a nested complex event pattern expression language called NEEL+, as an extension of the NEEL language, specifying nested CEP queries with predicates. We rigorously define the syntax and semantics of the NEEL+ language, with particular focus on predicate scoping and predicate placement. Accordingly, we introduce a top-down execution paradigm which recursively computes a nested NEEL+ query from the outermost query to the innermost one. We integrate predicate evaluation as part of the overall query evaluation process. Moreover, we design two optimization techniques that reduce the computation costs for processing NEEL+ queries. One, the intra-query method, called predicate push-in, optimizes each individual query component of a nested query by pushing the predicate evaluation into the process of computing the query rather than evaluating predicates at the end of the computation of that particular query. Two, the inter-query method, called predicate shortcutting, optimizes inter-query predicate evaluation. That is, it evaluates the predicates that correlate different query components within a nested query by exploiting a light weight predicate short cut. The NEEL+ system caches values of the equivalence attributes from the incoming data stream. When the computation starts, the system checks the existence of the attribute value of the outer query component in the cache and the predicate acts as a shortcut to early terminate the computation. Lastly, we conduct experimental studies to evaluate the CPU processing resources of the NEEL+ System with and without optimization techniques using real-world stock trading data streams. Our results confirm that our optimization techniques when applied to NEEL+ in a rich variety of cases result in a 10 fold faster query processing performance than the NEEL+ system without optimization."
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Ray, Medhabi. "Optimized Nested Complex Event Processing Using Continuous Caching." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/1060.

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"Complex Event Processing (CEP) has become increasingly important for tracking and monitoring anomalies and trends in event streams emitted from business processes such as supply chain management to online stores in e-commerce. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. While the state-of-the-art CEP systems mostly focus on the execution of flat sequence queries, we instead support the execution of nested CEP queries specified by the (NEsted Event Language) NEEL. However the iterative execution often results in the repeated recomputation of similar or even identical results for nested sub- expressions as the window slides over the event stream. This work proposes to optimize NEEL execution performance by caching intermediate results. In particular a method of applying selective caching of intermediate results called Continuous Sliding Caching technique has been designed. Then a further optimization of the previous technique which we call the Semantic Caching and the Continuous Semantic Caching have been proposed. Techniques for incrementally loading, purging and exploiting the cache content are described. Our experimental study using real- world stock trades evaluates the performance of our proposed caching strategies for different query types."
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Rozet, Allison M. "Shared Complex Event Trend Aggregation." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1379.

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Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends against high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing complex event trend aggregation poses several technical challenges. First, the execution of nested and diverse Kleene patterns is difficult to share. Second, we must share aggregate computation without the exponential costs of constructing the event trends. Third, not all sharing opportunities are beneficial because sharing aggregation introduces overhead. We propose a novel framework, Muse (Multi-query Snapshot Execution), that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. It adopts an online sharing strategy that eliminates re-computations for shared sub-patterns. To determine the beneficial sharing plan, we introduce a cost model to estimate the sharing benefit and design the Muse refinement algorithm to efficiently select robust sharing candidates from the search space. Finally, we explore optimization decisions to further improve performance. Our experiments over a wide range of scenarios demonstrate that Muse increases throughput by 4 orders of magnitude compared to state-of-the-art approaches with negligible memory requirements.
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Gillani, Syed. "Semantically-enabled stream processing and complex event processing over RDF graph streams." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES055/document.

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Résumé en français non fourni par l'auteur
There is a paradigm shift in the nature and processing means of today’s data: data are used to being mostly static and stored in large databases to be queried. Today, with the advent of new applications and means of collecting data, most applications on the Web and in enterprises produce data in a continuous manner under the form of streams. Thus, the users of these applications expect to process a large volume of data with fresh low latency results. This has resulted in the introduction of Data Stream Processing Systems (DSMSs) and a Complex Event Processing (CEP) paradigm – both with distinctive aims: DSMSs are mostly employed to process traditional query operators (mostly stateless), while CEP systems focus on temporal pattern matching (stateful operators) to detect changes in the data that can be thought of as events. In the past decade or so, a number of scalable and performance intensive DSMSs and CEP systems have been proposed. Most of them, however, are based on the relational data models – which begs the question for the support of heterogeneous data sources, i.e., variety of the data. Work in RDF stream processing (RSP) systems partly addresses the challenge of variety by promoting the RDF data model. Nonetheless, challenges like volume and velocity are overlooked by existing approaches. These challenges require customised optimisations which consider RDF as a first class citizen and scale the processof continuous graph pattern matching. To gain insights into these problems, this thesis focuses on developing scalable RDF graph stream processing, and semantically-enabled CEP systems (i.e., Semantic Complex Event Processing, SCEP). In addition to our optimised algorithmic and data structure methodologies, we also contribute to the design of a new query language for SCEP. Our contributions in these two fields are as follows: • RDF Graph Stream Processing. We first propose an RDF graph stream model, where each data item/event within streams is comprised of an RDF graph (a set of RDF triples). Second, we implement customised indexing techniques and data structures to continuously process RDF graph streams in an incremental manner. • Semantic Complex Event Processing. We extend the idea of RDF graph stream processing to enable SCEP over such RDF graph streams, i.e., temporalpattern matching. Our first contribution in this context is to provide a new querylanguage that encompasses the RDF graph stream model and employs a set of expressive temporal operators such as sequencing, kleene-+, negation, optional,conjunction, disjunction and event selection strategies. Based on this, we implement a scalable system that employs a non-deterministic finite automata model to evaluate these operators in an optimised manner. We leverage techniques from diverse fields, such as relational query optimisations, incremental query processing, sensor and social networks in order to solve real-world problems. We have applied our proposed techniques to a wide range of real-world and synthetic datasets to extract the knowledge from RDF structured data in motion. Our experimental evaluations confirm our theoretical insights, and demonstrate the viability of our proposed methods
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Books on the topic "Predictive Complex Event Processing"

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Hedtstück, Ulrich. Complex Event Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53451-9.

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Hedtstück, Ulrich. Complex Event Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61576-8.

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Bruns, Ralf, and Jürgen Dunkel. Complex Event Processing. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-09899-5.

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Fokin, Sergey. Improvement of technical means for processing waste from logging operations for fuel chips in felling conditions. ru: INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/24135.

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Currently, wood waste in the form of a dissected crown on the ground and the root fraction of the tree's biomass in the ground remain in felling areas, becoming potentially dangerous combustible materials in the event of forest fires, as well as obstacles to reforestation activities, and possible foci of infections. Shredding wood waste into wood chips will solve the problem of their disposal by using fuel chips as an additional source of heat energy. In the present work, the influence of design and operational parameters of milling machines with a modernized hydraulic system and equipped with active working bodies on the process of shredding wood waste is established. The annual economic effect from the introduction of the developed complex of wood waste shredding machines and economic indicators from the use of fuel chips are given. This publication is intended for undergraduates and postgraduates engaged in scientific research in the field of forestry mechanization.
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Architecting Complex-Event Processing Solutions with TIBCO. Brand: Addison-Wesley Professional, 2013.

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Hedtstück, Ulrich. Complex Event Processing: Verarbeitung von Ereignismustern in Datenströmen. Springer Vieweg, 2017.

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Hedtstück, Ulrich. Complex Event Processing: Verarbeitung von Ereignismustern in Datenströmen. Springer Vieweg, 2020.

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Abecker, Andreas, Opher Etzion, Adrian Paschke, and Nenad Stojanovic. Intelligent Complex Event Processing: Papers from the AAAI Spring Symposium. AAAI Press, 2009.

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Dunkel, Jürgen, and Ralf Bruns. Complex Event Processing: Komplexe Analyse von massiven Datenströmen mit CEP. Springer Vieweg, 2015.

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Luckham, David. Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Pearson Education, Limited, 2002.

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Book chapters on the topic "Predictive Complex Event Processing"

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Thoben, Klaus-Dieter, Abderrahim Ait-Alla, Marco Franke, Karl Hribernik, Michael Lütjen, and Michael Freitag. "Real-time Predictive Maintenance Based on Complex Event Processing." In Enterprise Interoperability, 291–96. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119564034.ch36.

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Mousheimish, Raef, Yehia Taher, and Karine Zeitouni. "autoCEP: Automatic Learning of Predictive Rules for Complex Event Processing." In Service-Oriented Computing, 586–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46295-0_38.

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Geng, Shaofeng, Xiaoxi Guo, Jia Zhang, Yongheng Wang, Renfa Li, and Binghua Song. "A Prediction Method Based on Complex Event Processing for Cyber Physical System." In Communications in Computer and Information Science, 283–92. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8890-2_20.

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Steele, Guy L., Xiaowei Shen, Josep Torrellas, Mark Tuckerman, Eric J. Bohm, Laxmikant V. Kalé, Glenn Martyna, et al. "Complex Event Processing." In Encyclopedia of Parallel Computing, 352. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2480.

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Etzion, Opher. "Complex Event Processing." In Encyclopedia of Database Systems, 530–31. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_571.

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Etzion, Opher. "Complex Event Processing." In Encyclopedia of Database Systems, 412–13. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_571.

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Etzion, Opher. "Complex Event Processing." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_571-2.

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Hedtstück, Ulrich. "Einführung mit typischen Anwendungen." In Complex Event Processing, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61576-8_1.

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Hedtstück, Ulrich. "Abgrenzung des CEP zu anderen Methoden des Data Analytics." In Complex Event Processing, 119–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61576-8_10.

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Hedtstück, Ulrich. "Anhang: Prädikatenlogik." In Complex Event Processing, 125–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61576-8_11.

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Conference papers on the topic "Predictive Complex Event Processing"

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Fülöp, Lajos Jenő, Árpád Beszédes, Gabriella Tóth, Hunor Demeter, László Vidács, and Lóránt Farkas. "Predictive complex event processing." In the Fifth Balkan Conference in Informatics. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2371316.2371323.

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Tóth, Gabriella, Lajos Jenő Fülöp, László Vidács, Árpád Beszédes, Hunor Demeter, and Lóránt Farkas. "Complex event processing synergies with predictive analytics." In the Fourth ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1827418.1827436.

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Mousheimish, Raef, Yehia Taher, and Karine Zeitouni. "Automatic learning of predictive rules for complex event processing." In DEBS '16: The 10th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2933267.2933430.

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Zacheilas, Nikos, Vana Kalogeraki, Nikolas Zygouras, Nikolaos Panagiotou, and Dimitrios Gunopulos. "Elastic complex event processing exploiting prediction." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363758.

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Christ, Maximilian, Julian Krumeich, and Andreas W. Kempa-Liehr. "Integrating Predictive Analytics into Complex Event Processing by Using Conditional Density Estimations." In 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW). IEEE, 2016. http://dx.doi.org/10.1109/edocw.2016.7584363.

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Gillani, Syed, Abderrahmen Kammoun, Kamal Singh, Julien Subercaze, Christophe Gravier, Jacques Fayolle, and Frederique Laforest. "Pi-CEP: Predictive Complex Event Processing Using Range Queries over Historical Pattern Space." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.167.

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Govindasamy, V., R. Ganesh, G. Nivash, and S. Shivaraman. "Prediction of events based on Complex Event Processing and Probabilistic Fuzzy Logic." In 2014 International Conference On Computation of Power , Energy, Information and Communication (ICCPEIC). IEEE, 2014. http://dx.doi.org/10.1109/iccpeic.2014.6915414.

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Kim, Yoon-Ki, and Chang-Sung Jeong. "Risk Prediction System Based on Risk Prediction Model with Complex Event Processing: Risk Prediction in Real Time on Complex Event Processing Engine." In 2014 IEEE International Conference on Big Data and Cloud Computing (BdCloud). IEEE, 2014. http://dx.doi.org/10.1109/bdcloud.2014.43.

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Lang, Jan, and Zdenko Capik. "Prediction based on hybrid method in complex event processing." In 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2014. http://dx.doi.org/10.1109/sami.2014.6822430.

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Turchin, Yulia, Avigdor Gal, and Segev Wasserkrug. "Tuning complex event processing rules using the prediction-correction paradigm." In the Third ACM International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1619258.1619272.

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Reports on the topic "Predictive Complex Event Processing"

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Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.
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