An open architecture for event-based analytics

Milošević, Zoran; Chen, Weisi; Berry, Andrew; Rabhi, Fethi A.

doi:10.1007/s41060-016-0029-7

Cited by 12 publications

(6 citation statements)

References 19 publications

(45 reference statements)

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“…Our methodology can be integrated with WordNet (Fellbaum, 2012) to link process ontology classes with their synonyms. Furthermore, the developed process ontology can also be used as a knowledge base for question answering systems or enterprise search engines for exploring process information as well as relating to the domain ontologies or data analytics (Milosevic et al, 2016) of the corresponding domain. Taking advantages of the Semantic Web technologies, the process ontology can be turned into a knowledge graph of processes and can be used for graph mining to provide recommendation systems based on the similarities found between different processes.…”

Section: Discussionmentioning

confidence: 99%

Process ontology development using natural language processing: a multiple case study

Gürbüz

Rabhi

Demirörs

2018

BPMJ

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Purpose Integrating ontologies with process modeling has gained increasing attention in recent years since it enhances data representations and makes it easier to query, store and reuse knowledge at the semantic level. The authors focused on a process and ontology integration approach by extracting the activities, roles and other concepts related to the process models from organizational sources using natural language processing techniques. As part of this study, a process ontology population (PrOnPo) methodology and tool is developed, which uses natural language parsers for extracting and interpreting the sentences and populating an event-driven process chain ontology in a fully automated or semi-automated (user assisted) manner. The purpose of this paper is to present applications of PrOnPo tool in different domains. Design/methodology/approach A multiple case study is conducted by selecting five different domains with different types of guidelines. Process ontologies are developed using the PrOnPo tool in a semi-automated and fully automated fashion and manually. The resulting ontologies are compared and evaluated in terms of time-effort and recall-precision metrics. Findings From five different domains, the results give an average of 70 percent recall and 80 percent precision for fully automated usage of the PrOnPo tool, showing that it is applicable and generalizable. In terms of efficiency, the effort spent for process ontology development is decreased from 250 person-minutes to 57 person-minutes (semi-automated). Originality/value The PrOnPo tool is the first one to automatically generate integrated process ontologies and process models from guidelines written in natural language.

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Section: Discussionmentioning

confidence: 99%

Process ontology development using natural language processing: a multiple case study

Gürbüz

Rabhi

Demirörs

2018

BPMJ

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“…• There is currently no standard method for defining events, event pattern types and rule operators, and consequently no unified approach for exchange of event pattern instances between different CEP systems. This causes issues when several big data, analytics solutions and techniques are needed to be linked together [128]. An open approach with the capability to leverage current technologies such as R [129], web services, cloud analytics from Azure [130] and REST [131] will enhance the CEP technology.…”

Section: Challenges and Recommendationsmentioning

confidence: 99%

Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendations

et al. 2022

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A datacentre stores information and manages data access in fast and reliable manner. Failure of datacentre operation is not an option and can be catastrophic. Internet of things (IoT) devices in datacentre can automate management tasks and reduce human intervention and error. IoT devices can be used to manage many datacentre routine tasks such as monitoring physical infrastructure, updating software and configuration, monitoring network traffic, and automating alerting reports to respective authorities. The physical and cyber security of the datacentre can be handled by IoT technology by intrusion detection methods. By 2025, more than 25 billion things will be connected to the internet network, therefore massive data will be generated by different heterogeneous sources, and powerful processing engines such as complex event processing (CEP) are needed to handle such a fast and continuous stream of big data. The integration of machine learning (ML) and deep learning (DL) can enhance CEP by introducing new features such as automated rule extraction and self-healing mechanism. This study aims to provide an overview of CEP, as well as its features and potential for integration with IoT applications and ML/DL techniques. We provide a review of recent research works to highlight the capability and applicability of CEP technology to monitor physical facilities and cyber security in detail. This review also highlights several issues and challenges, and provides suggestions for future research. The highlighted insights and recommendations in this paper could raise efforts toward the development of future datacentres based on CEP technology.

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“…Event analysis must be conducted by batch processing of historical events. The event pattern must support mathematical, logical, and statistical patterns among events [21]. An event study involves several steps and many choices must be made at each step.…”

Section: Literature Surveymentioning

confidence: 99%

Building a Calendar of Events Database by Analyzing Financial Spikes

et al. 2021

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An event is a piece of news that triggers a change in stock prices. Here, an event study is undertaken to capture the effect of abnormal returns due to an event. The event can affect the stock market in the long term or short term. Event research is relevant to both the efficient market hypothesis and behavioral finance. In this study, we collected data from websites that manage financial and economic data, performed a sentiment analysis, and correlated news article data with changes in a particular company's stock prices in the stock market. Data were collected from two well-known financial news websites. We observed a correlation between stock prices and news items. An event period of one day was considered for the study. The regression equation determined the relationship between stock returns and polarity and subjectivity. Bayesian model averaging was performed to identify the effects of polarity and subjectivity on stock returns. Time-series data were decomposed into components and detrended via regression. Prominent keywords and their polarity values for a particular day were plotted. An event enhanced some stock returns while adversely affecting other stocks. We found a variation range of -400 to 200 for different company stocks for the selected period.

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An open architecture for event-based analytics

Cited by 12 publications

References 19 publications

Process ontology development using natural language processing: a multiple case study

Process ontology development using natural language processing: a multiple case study

Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendations

Building a Calendar of Events Database by Analyzing Financial Spikes

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