Data Mining for the Internet of Things: A Survey

Kumar, Mukesh; Kumar, Santosh; Yadav, Saneh Lata

doi:10.1201/9781003369066-8

Cited by 2 publications

(1 citation statement)

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“…Text mining [21][22][23][24][25][26][27], also referred to as text data mining or text analytics, is an interdisciplinary field that integrates various technologies, including natural language processing (NLP), machine learning, computer science, and statistical methods. The primary objective of text mining is to automatically extract knowledge and meaningful information from unstructured or semi-structured natural language text documents.…”

Section: Text Miningmentioning

confidence: 99%

Finite State Automata on Multi-Word Units for Efficient Text-Mining

Postiglione

2024

Mathematics

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Text mining is crucial for analyzing unstructured and semi-structured textual documents. This paper introduces a fast and precise text mining method based on a finite automaton to extract knowledge domains. Unlike simple words, multi-word units (such as credit card) are emphasized for their efficiency in identifying specific semantic areas due to their predominantly monosemic nature, their limited number and their distinctiveness. The method focuses on identifying multi-word units within terminological ontologies, where each multi-word unit is associated with a sub-domain of ontology knowledge. The algorithm, designed to handle the challenges posed by very long multi-word units composed of a variable number of simple words, integrates user-selected ontologies into a single finite automaton during a fast pre-processing step. At runtime, the automaton reads input text character by character, efficiently locating multi-word units even if they overlap. This approach is efficient for both short and long documents, requiring no prior training. Ontologies can be updated without additional computational costs. An early system prototype, tested on 100 short and medium-length documents, recognized the knowledge domains for the vast majority of texts (over 90%) analyzed. The authors suggest that this method could be a valuable semantic-based knowledge domain extraction technique in unstructured documents.

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Section: Text Miningmentioning

confidence: 99%

Finite State Automata on Multi-Word Units for Efficient Text-Mining

Postiglione

2024

Mathematics

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Predictive Maintenance with Linguistic Text Mining

Postiglione,

Monteleone

2024

Mathematics

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The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency.

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Data Mining for the Internet of Things: A Survey

Cited by 2 publications

References 0 publications

Finite State Automata on Multi-Word Units for Efficient Text-Mining

Finite State Automata on Multi-Word Units for Efficient Text-Mining

Predictive Maintenance with Linguistic Text Mining

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