Rule‐based preprocessing for data stream mining using complex event processing

Ramírez, Aurora; Moreno, Nathalie; Vallecillo, Antonio

doi:10.1111/exsy.12762

Cited by 9 publications

(4 citation statements)

References 63 publications

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“…Ramrez et al proposed a rule‐based system (Ramírez et al, 2021) and a CEP for formulating the query language expressed in the preprocessing approach. They work was evaluated using various real‐time scenarios, which demonstrate that CEP rules can provide an effective language for preprocessing tasks and help to solving the complexity of decision models.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

Rule based complex event processing for IoT applications: Review, classification and challenges

Kumar,

Agarwal

2024

Expert Systems

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In recent decades, data aggregation and correlation have emerged as a significant and challenging area of research for finding useful relationships between different features of streaming data. Similarly, complex event processing (CEP) has emerged as a vital tool for aggregating and finding patterns from streaming data such as video streaming, real‐time stock trades, sensor data and more. In this context, rule‐based classifier algorithms are widely employed to discover valuable patterns from streaming data and cause‐and‐effect relationships between events. However, streaming data is dynamic and complex in nature, and it is not feasible to update the rules manually by domain experts continuously. On the contrary, dynamic data sometimes fails to adopt rules, which leads to sub optimal CEP implementation. Rule‐based CEP systems come up with their own set of challenges, which primarily includes rules adaptability for streaming IoT data. In dynamic environment, shifting patterns in data streams can impact the performance of the CEP system, as well as the correlation between events, and finding useful patterns is very much challenging. In this article, our objective is to provide a comprehensive survey for CEP using rule‐based algorithms applied across various domain and applications. We present a broad literature review using a sequential expansion of methods used for CEP, which primarily include event producers, preprocessing of events, robust rule implementation, and decision support. Additionally, we present a detailed classification of approaches used for different applications. Finally, after applying the rigorous review, it was feasible to present the state‐of‐the‐art research, which focuses on designing robust rules, application specific insights, scalable event‐driven architectures and finding the domain's trends with significant challenges and future scope.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

Rule based complex event processing for IoT applications: Review, classification and challenges

Kumar,

Agarwal

2024

Expert Systems

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“…Repeat these instructions for the desired number of episodes and a total reward has been obtained. 10. A new layer has been added that outputs the new Q value.…”

Section: Damaged Leaf Images Have Been Chosen Randomlymentioning

confidence: 99%

“…As a consequence, this was examined if mass spectral information might be utilized to determine molecular substructure. Since the development of deep learning, various systems based on recurrent neural networks have exhibited incredible promise when dealing with the problem in preprocessing of the sequence data stream [10]. To measure and match the chemical molecular patterns of the pesticide residue, some methods have been proposed, such as plant disease net and Alex Net CNN.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

Sellamuthu¹,

Kaliappan²

2023

Computer Systems Science and Engineering

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Pesticides have become more necessary in modern agricultural production. However, these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem. Due to a shortage of basic pesticide exposure awareness, farmers typically utilize pesticides extremely close to harvesting. Pesticide residues within foods, particularly fruits as well as veggies, are a significant issue among farmers, merchants, and particularly consumers. The residual concentrations were far lower than these maximal allowable limits, with only a few surpassing the restrictions for such pesticides in food. There is an obligation to provide a warning about this amount of pesticide use in farming. Previous technologies failed to forecast the large number of pesticides that were dangerous to people, necessitating the development of improved detection and early warning systems. A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified, named as the Hybrid Chronic Multi-Residual Framework (HCMF), in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing (CEP) by processing given spatial and sequential data. The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers. Overall, the technique is carried out in a Python environment, with the results showing that the proposed model has a 98.57% accuracy and a training loss of 0.30.

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“…Instead, the Sport Sparks tool was engineered to combine different forms of rule‐based reasoning to deliver the planned high automation. Rule‐based expert systems continue to be applied to solve real‐world problems, for example, to detect events during data pre‐processing (Ramírez et al, 2021), personalize medical education (Quinn et al, 2017), and detect inconsistencies in domain engineering processes (Elfaki, 2016), as well as to deliver benefits in domains such as medical billing (Abdullah et al, 2017) and e‐government (Hossain et al, 2015). A rule‐based reasoning technique was selected to implement the Generating guideline and automate the generation of partial and/or complete coaching ideas.…”

Section: Designing a Co‐creative Ai Tool For Elite Athlete Coachingmentioning

confidence: 99%

Designing new digital tools to augment human creative thinking at work: An application in elite sports coaching

et al. 2022

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Creative thinking is desirable in many professions. This article reports new research that followed a design science approach to develop and investigate a co-creative tool called Sport Sparks in one professionthe coaching of professional football players.In response to a coach entering a text description of a coaching challenge (e.g., struggling to maintain the fitness of an athlete) into the tool, the tool automatically generated potentially novel ideas (e.g., reducing game time and changing their nutrition) that the coach could select and/or adapt and evolve into a simple action plan (e.g., which links nutrition to increased game time). This Sport Sparks tool was designed to be an example of human-centred artificial intelligence that aspires to empower humans, to deliver high levels of human control as well as automation, and empower people rather than emulate their expertise. It was engineered with rulebased reasoning to automate the generation of potential new ideas that coaches could select and refine during interactions which provide high user control over this automation. The potential of such a co-creative tool, and value of the guidelines, were demonstrated during the tool's evaluation by coaching practitioners at a Premier League football club. The practitioners used the tool to generate new ideas to coaching challenges, and reported evidence of different forms of creative thinking, although some also reported the need for more support for creative collaborations and solution planning. The paper ends by discussing future directions for both the Sport Sparks tool and other co-creative AI tools.co-creative AI tools, creativity, design guidelines, elite coaching, human-centred AI, sports | INTRODUCTIONCreativity is defined as the ability to produce work that is novel and original, as well as appropriate and useful (Sternberg, 1999). The need for more creative thinking and outcomes is well-documented, for example, to solve complex problems (e.g., Isaksen et al., 2011) and facilitate innovation (e.g., Design Council, 2011). Digital tools to support human creativity and more creative engagement has become one of the means to meet this need (e.g., Candy, 2007). People use these tools to, for example, discover new information, synthesize novel content from existing material,

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Rule‐based preprocessing for data stream mining using complex event processing

Cited by 9 publications

References 63 publications

Rule based complex event processing for IoT applications: Review, classification and challenges

Rule based complex event processing for IoT applications: Review, classification and challenges

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

Designing new digital tools to augment human creative thinking at work: An application in elite sports coaching

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