scholarly journals INFORMATION TECHNOLOGY OF SEARCHING COMPARATIVE COMPANIES FOR BUSINESS VALUATION USING INTELLIGENT AGENTS

2019 ◽  
pp. 99-106
Author(s):  
Anastasia Evgenievna Kvyatkovskaya ◽  
Anna Olegovna Polumordvinova ◽  
Irina Yurievna Kvyatkovskaya ◽  
Elena Vitalievna Chertina
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Information Processing ◽  
Data Processing ◽  
Information Search ◽  
Intelligent Agents ◽  
Multi Agent Systems ◽  
Business Valuation ◽  
Object Of Study ◽  
Metric Information

The paper deals with developing information technology of data processing for solving business valuation problems based on the selection of comparative companies. An emerging IT company or a startup are taken as the object of study, for which traditional valuation methods are not apt. In terms of the research task there has been introduced the concept of a precedent - a company for which the search of analogues is being realized. The basic concepts of multi-agent systems are used. Four types of intelligent agents are presented that provide the implementation of the elements of the end-to-end technology: information search of objects with specified properties, intelligent processing of user requests, monitoring of objects, data mining. Stage sequence of the distributed decision-making technology has been considered. There are given metric and non-metric information processing mechanisms about analogues using metrics and proximity measures. The problem of non-metric information processing has been formulated. The tasks of data mining analysis have been defined, which are vital for monitoring the IT-companies in order to evaluate the business. The presented conclusions are summarized in the form of a complex information technology of data processing.

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

Information Processing Biases in Impairment Decisions: Effect of Reversibility of Impairment Losses and Disclosure Transparency

2018 ◽  
Vol 30 (2) ◽  
pp. 77-94
Author(s):  
Hwee Cheng Tan ◽  
Ken T. Trotman
Keyword(s):  
Information Processing ◽  
Information Search ◽  
Jel Classification ◽  
Regulatory Requirements ◽  
Experimental Conditions ◽  
Information Evaluation ◽  
Evaluation Bias ◽  
Information Processing Biases ◽  
Search Bias

ABSTRACT We investigate the effect of regulatory requirements on impairment decisions and managers' search for and evaluation of impairment information. We manipulate reversibility of impairment losses (“can be reversed” versus “cannot be reversed”) and transparency in disclosures of impairment assumptions (more transparent versus less transparent) in a 2 × 2 experiment. We find that managers are more willing to impair when impairment losses can be reversed than when they cannot be reversed, but this effect does not vary with disclosure transparency. We also find that managers display information search bias in all four experimental conditions, however, regulatory requirements do not result in differences in the level of information search bias across the conditions. In contrast, regulatory requirements affect the differences in the level of information evaluation bias across conditions. We find that when impairment losses cannot be reversed, information evaluation bias is higher when disclosures are more transparent than less transparent. JEL Classification: M40; M41.

A Meta-Mining Ontology Framework for Data Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 37-56
Author(s):  
Man Tianxing ◽  
Nataly Zhukova ◽  
Alexander Vodyaho ◽  
Tin Tun Aung
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Streams ◽  
Specific Data ◽  
Ontology Merging ◽  
Knowledge Models ◽  
Task Requirements ◽  
Ontology Extraction ◽  
Merging Method ◽  
And Task

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

scholarly journals Data Mining and Intelligent Agents for Supporting Mass Customization in the Automotive Industry

10.5772/13135 ◽  
2011 ◽  
Author(s):  
Efthimia Mavridou ◽  
Dimitrios Tzovaras ◽  
Evangelos Beakiaris ◽  
Pavlos Spanidis ◽  
Maria Gemou ◽  
...  
Keyword(s):  
Data Mining ◽  
Automotive Industry ◽  
Mass Customization ◽  
Intelligent Agents

Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

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