Application of Data Mining techniques to identify relevant Key Performance Indicators

Peral, Jesús; Maté, Alejandro; Marco, Manuel

doi:10.1016/j.csi.2016.11.006

Cited by 56 publications

(44 citation statements)

References 17 publications

(15 reference statements)

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“…Data Mining (DM) представља методу претраживања података која је, c развојем рачунарске технологије, дoниjeлa ефикаснocт при претраживању великих количина сирових података [3]. Data Mining је процес екстраховања претходно непознатих, ваљаних и дјелотворних информација из великих база података и коришћења тих информација за доношење кључних пословних одлука [4].…”

Section: примјена претраживања податакаunclassified

Предвиђање Резултата Консултантских Услуга У Грађевинарству

Станаревић¹

2016

AGG+

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Универзитет у Бањој Луци I Архитектонско-грађевинско-геодетски факултет University of Banja Luka I Faculty of architecture, civil engineering and geodesy ISSN 2303-6036 UDK I UDC 72 AГГ+ часопис за архитектуру, грађевинарство, геодезију и сродне научне области ACEG+ Journal for Architecture, Civil Engineering, Geodesy and other related scientific fields 058-067Оригинални научни рад I Original scientific paper UDK I UDC 624.14:519 ПРЕДВИЂАЊЕ РЕЗУЛТАТА КОНСУЛТАНТСКИХ УСЛУГА У ГРАЂЕВИНАРСТВУ АПСТРАКТПриказана је могућност вредновања и предвиђања консултантских услуга у грађевинској индустрији. Предложен је модел који се заснива на свеобухватној анализи великог броја утицаја на грађевинску производњу, промјенљивих у простору и времену. У раду су приказани резултати спроведених лицитација (надметања) током једне године. Примијењена је метода дескриптивне статистике и метода претраживања података, са форсирањeм варијабли и без форсирања варијабли. Оваква процедура се може спровести и за остале године у току неког посматраног периода. Oмогућена je предикција, односно yтвpђeнe могућности доношења правовремених одлука у неизвјесним условима пословања. Кључне ријечи: : консултантске услуге, грађевинска индустрија, вредновање, предикција, дескриптивна статистика, претраживање података PREDICTION OF CONSULTANCY SERVICES RESULTS IN CONSTRUCTION INDUSTRY ABSTRACTThe possibility of evaluation and prediction of consultancy services in the construction industry is presented. The model is based on a comprehensive analysis of a large numbers of impacts on the construction production, varying in space and time. The paper presents the results of the conducted auction (bidding) for one year. Descriptive statistics method as well as data mining method, with and without forcing the variables, is applied. This procedure can also be carried out for other years during an observation period. The prediction, as well as the ability to make timely decisions in uncertain business conditions, is enabled.

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Section: примјена претраживања податакаunclassified

Предвиђање Резултата Консултантских Услуга У Грађевинарству

Станаревић¹

2016

AGG+

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“…In the current era, data mining has given a great deal of concern in the wide range application fields such as education [6], finance [7], manufacturing [8], healthcare [9], business [10], telecommunication [11], agriculture [12], and customer relationship management [13]. Furthermore, the most commonly data mining tasks include: classification, clustering, association rules, regression analysis, and decision tree [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Developing Classification Model to Investigate the Problem of Computing Students Studies Length

Abdalla¹,

Aborokbah²,

Elfaki³

2019

IJMLC

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Students are the key asset for any higher education institutions and their success in achieving the best academic performance within study planned period are resulting in producing graduates with high educational and professional quality skills. These graduates will become great leaders, manpower and play an important role in country's economic and social development. In most Higher Education Institutions, the students' studies length problem has not been investigated comprehensively despite the seriousness of this problem and its impact in the long and short terms. The aim of this research is to develop mining classification model based on decision tree to support academic administrators in decision making by defining features of linger students which could be used to develop an early warning system that has the ability to predict the students "who" might exceed the planned study length period. Data from faculty of computers and information technology at Uuniversity of Tabuk, KSA, has been collected using a survey method; students from male and female sections are participated in this survey. Then the data is preprocessed, after preprocessing of the data, C4.5 algorithm has been applied to discover the classifications.Index Terms-Data mining, classification, computer in education, C4.5, linger students commas.

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“…Data mining [13,14] refers to the process of discovering hidden information from massive amounts of data. The correlation study on the data and the extraction of key information from massive monitoring data is the primary content for establishing a seepage detection model given the increasing volume of monitoring data.…”

Section: Modeling Processmentioning

confidence: 99%

Detection Model for Seepage Behavior of Earth Dams Based on Data Mining

Jiang

2018

Mathematical Problems in Engineering

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Seepage behavior detecting is an important tool for ensuring the safety of earth dams. However, traditional seepage behavior detection methods have used insufficient monitoring data and have mainly focused on single-point measures and local seepage behavior. The seepage behavior of dams is not quantitatively detected based on the monitoring data with multiple measuring points. Therefore, this study uses data mining techniques to analyze the monitoring data and overcome the above-mentioned shortcomings. The massive seepage monitoring data with multiple points are used as the research object. The key information on seepage behavior is extracted using principal component analysis. The correlation between seepage behavior and upstream water level is described as mutual information. A detection model for overall seepage behavior is established. Result shows that the model can completely extract the seepage monitoring data with multiple points and quantitatively detect the overall seepage behavior of earth dams. The proposed method can provide a new and reasonable means of quantitatively detecting the overall seepage behavior of earth dams.

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Application of Data Mining techniques to identify relevant Key Performance Indicators

Cited by 56 publications

References 17 publications

Предвиђање Резултата Консултантских Услуга У Грађевинарству

Предвиђање Резултата Консултантских Услуга У Грађевинарству

Developing Classification Model to Investigate the Problem of Computing Students Studies Length

Detection Model for Seepage Behavior of Earth Dams Based on Data Mining

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