scholarly journals TEXT AND DATA MINING TECHNIQUES IN ASPECT OF KNOWLEDGE ACQUISITION FOR DECISION SUPPORT SYSTEM IN CONSTRUCTION INDUSTRY / DUOMENŲ RINKIMO METODAI STATYBOS SPRENDIMŲ PARAMOS SISTEMAI

2010 ◽  
Vol 16 (2) ◽  
pp. 219-232 ◽  
Author(s):  
Marcin Gajzler

This article presents the possibilities of using mining techniques in building Decision Support Systems. One of the biggest problems is the issue of gaining data and knowledge, their mutual representation and reciprocal usage. Data and knowledge make up the resources of the system and are its key link. It has been estimated that 70% to 80% of the sources available for general use are text documents. The text mining technique is defined as a process aiming to extract previously unknown information from text resources (e.g. technological cards). The fundamental feature of text mining is the ability to converse text documents in formal form, which opens up great possibilities of conducting further analysis. This article presents chosen IT tools using text mining technique, along with the elements of the text mining analysis. The main objectives are the simplification of the process of knowledge acquisition, its automation and shortening as well as the creation of ready‐made models containing knowledge. Previous tests with knowledge acquisition (surveys, questionnaires) were time‐consuming and exacting for experts. Santrauka Straipsnyje pateikiamos informacijos rinkimo metodu pritaikymo galimybės sprendimų paramos sistemoms statyboje. Daugiausia problemų sukelia informacijos gavimas, tinkamas jos atvaizdavimas ir naudojimas. Duomenys yra pagrindinis sistemos išteklius. Nustatyta, kad nuo 70 iki 80 % visu turimų bendrojo naudojimo informacijos šaltinių yra tekstiniai dokumentai. Tekstines informacijos rinkimo technika yra suprantama kaip procesas, kuriuo siekiama išgauti anksčiau nežinoma informacija iš tekstiniu dokumentu (pavyzdžiui, technologiniu kortelių). Pagrindine šios technikos savybė ‐ galimybė tekstinių dokumentų informacija pateikti formalizuota forma, tai atveria plačiu galimybių tolesnei analizei. Šiame straipsnyje pateikiamos pasirinktos IT priemonės, naudojamos tekstinei informacijai rinkti. Autoriaus tikslas ‐ su paprastinti informacijos rinkimą, ji automatizuoti ir sutrumpinti, sukurti informacija apimančius modelius. Ankstesni informacijos kaupimo metodai (apklausos, anketos) reikalavo daug ekspertų darbo ir laiko.

2021 ◽  
pp. 4101-4109
Author(s):  
Baraa Hasan Hadi ◽  
Tareef Kamil Mustafa

The majority of systems dealing with natural language processing (NLP) and artificial intelligence (AI) can assist in making automated and automatically-supported decisions. However, these systems may face challenges and difficulties or find it confusing to identify the required information (characterization) for eliciting a decision by extracting or summarizing relevant information from large text documents or colossal content.   When obtaining these documents online, for instance from social networking or social media, these sites undergo a remarkable increase in the textual content. The main objective of the present study is to conduct a survey and show the latest developments about the implementation of text-mining techniques in humanities when summarizing and eliciting automated decisions. This process relies on technological advancement and considers (1) the automated-decision support-techniques commonly used in humanities, (2) the performance evolution and the use of the stylometric approach in text-mining, and (3) the comparisons of the results of chunking text by using different attributes in Burrows' Delta method. This study also provides an overview of the efficiency of applying some selected data-mining (DM) methods with various text-mining techniques to support the critics' decision in artistry ‒ one field of humanities. The automatic choice of criticism in this field was supported by a hybrid approach to these procedures.


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