Applying Data Mining Techniques to Discover KPIs Relationships in Business Process Context

2017 ◽  
Author(s):  
Emna Ammar El Hadj Amor ◽  
Sonia Ayachi Ghannouchi
Keyword(s):  
Data Mining ◽  
Business Process ◽  
Data Mining Techniques ◽  
Process Context

A Framework to Analyze Business Process Log in XML Format

2021 ◽  
Vol 12 (3) ◽  
pp. 2623-2630
Author(s):  
Ang Jin Sheng Et.al
Keyword(s):  
Data Mining ◽  
Business Process ◽  
Accurate Result ◽  
Structured Data ◽  
Web Pages ◽  
Web Searching ◽  
General Application ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Relationship

XML has numerous uses in a wide variety of web pages and applications. Some common uses of XML include tasks for web publishing, web searching and automation, and general application such as for utilize, store, transfer and display business process log data. The amount of information expressed in XML has gone up rapidly. Many works have been done on sensible approaches to address issues related to the handling and review of XML documents. Mining XML documents offera way to understand both the structure and the content of XML documents. A common approach capable of analysing XML documents is frequent subtree mining.Frequent subtree mining is one of the data mining techniques that finds the relationship between transactions in a tree structured database. Due to the structure and the content of XML format, traditional data mining and statistical analysis hardly applied to get accurate result. This paper proposes a framework that can flatten a tree structured data into a flat and structured data, while preserving their structure and content.Enabling these XML documents into relational structured data allows a range of data mining techniques and statistical test can be applied and conducted to extract more information from the business process log.

scholarly journals Data Mining Techniques Based Stock Record Management

2019 ◽  
pp. 505-509
Author(s):  
N. Vijayalakshmi ◽  
A. Baviya
Keyword(s):  
Data Mining ◽  
Business Process ◽  
Fast Moving ◽  
Frequent Pattern ◽  
Record Management ◽  
Data Mining Techniques ◽  
Factors Influencing ◽  
Business Support ◽  
Slow Moving ◽  
Stock Information

Our targets are to show signs of improvement basic leadership for enhancing deal, administrations and quality, which is helpful instrument for business support, speculation and reconnaissance. A methodology is actualized for mining examples of gigantic stock information to foresee factors influencing the clearance of items. For this gap the stock information in three distinct groups based on sold amounts Dead-Stock, Slow-Moving and Fast-Moving utilizing K- implies calculation or Hierarchical agglomerative calculation. After that Most Frequent Pattern calculation is executed to discover frequencies of property estimations of the relating things. Most Frequent Pattern gives visit examples of thing characteristics and furthermore gives deals incline in a minimal shape. Grouping and Most Frequent Pattern calculation can create increasingly valuable example from expansive stock information which is useful to get thing data for stock. Opportune recognizable proof of recently developing patterns is required in business process. Information mining procedures are most appropriate for the characterization, valuable examples extraction and predications which are vital for business support and basic leadership. Examples from stock information show advertise inclines and can be utilized in determining which has incredible potential for basic leadership, vital arranging.

Path Mining in Web Processes Using Profiles

2011 ◽  
pp. 896-901 ◽  
Author(s):  
Jorge Cardoso
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Business Process ◽  
Management System ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Data Mining Techniques ◽  
Using Data

Business process management systems (BPMSs) (Smith & Fingar, 2003) provide a fundamental infrastructure to define and manage business processes, Web processes, and workflows. When Web processes and workflows are installed and executed, the management system generates data describing the activities being carried out and is stored in a log. This log of data can be used to discover and extract knowledge about the execution of processes. One piece of important and useful information that can be discovered is related to the prediction of the path that will be followed during the execution of a process. I call this type of discovery path mining. Path mining is vital to algorithms that estimate the quality of service of a process, because they require the prediction of paths. In this work, I present and describe how process path mining can be achieved by using data-mining techniques.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Survey of Data Mining Techniques Used for Real Time Churn Prediction

2017 ◽  
Vol 7 (3) ◽  
pp. 219-222 ◽  
Author(s):  
Jean Claude Turiho ◽  
◽  
Wilson Cheruiyot ◽  
Anne Kibe ◽  
Irénée Mungwarakarama ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Churn Prediction ◽  
Data Mining Techniques

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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