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Knowledge graphs play a central role in big data integration, especially for connecting data from different domains. Bringing unstructured texts, e.g. from scientific literature, into a structured, comparable format is one of the key assets. Here, we use knowledge graphs in the biomedical domain working together with text mining based document data for knowledge extraction and retrieval from text...
The relationship between parameter configurations of mechanical products are the main design rules that affect their work performance and the major factor that optimize product structure. However, the actual relationship of parameter configurations can't be gotten directly in the design specifications or the manual. Physical experimental methods are difficult to implement due to long cycle and high...
Data Mining can find out the hidden important information in mass data, whose main purpose is to help decision-makers to look for the potential relevance between the data, to find out neglected elements, and make decision based on these models automatically. This paper, based on the discussion of data mining, analyzes the framework of enterprise decision support system, designs the enterprise decision...
In order to realize the objective and synthetic evaluation of the feasibility in Low-carbon (LC) project, the paper constructs the knowledge representation system(i.e. attribute value of information system), applies the reduction and the mining rules of the Rough Set Theory, at same the time computing dynamic weight, subjective weight, objective weight are combined with Analytic Hierarchy Process...
According to the current situation in emergency management, this paper aims to solve the problem of difficult in searching needed knowledge in emergency management. From the perspective of knowledge system engineering, this paper conducts an in-depth study on knowledge-mined model of emergency documents and offers an alternative for decision-makers to locate and use emergency document knowledge more...
Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld...
To enhance the ability of intelligent reasoning and decision-making modules of present MIS, an algorithm is proposed which using rough set to reduce sampling data and produce rule library. It is done by giving a knowledge reduction and rule extraction algorithm based on a comprehensive analysis of rough set theory and present algorithms, taking a comprehensive evaluation database of university students...
This paper aims at studying the roles of social features (as obtained from social networking sources) in buyers' decision process when they are searching for products to buy. Through close observation of users' objective behavior, we have discovered the importance of different types of social features in supporting users to achieve a confident decision at the end. Improving suggestions are further...
Social networks of the Web 2.0 have become global (e.g. FaceBook, MSN). In 1977, L. C. FREEMAN published the first generic metrics for Social Networks Analysis (SNA), mainly based on static graph-mining models. The objective of our work is to introduce new dynamic SNA models dedicated to SNA and to take the conceptual aspects of enterprises and institutions social graph into account. Our work is based...
Sensors are being deployed to improve border security generating enormous collections of data and databases. Unfortunately these sensors can respond to a variety of stimuli, sometimes reacting to meaningful events and sometimes triggered by random events which are considered false alarms. The intent of this project is to supplement human intelligence in a sensor network framework that can assist in...
Nowadays, it is certain that we have to deal with massive data and abstract implicit regularity from massive data. In this paper, the reduction algorithm based on Rough Sets is proposed as a practical data mining technology and the procedure of telecommunication decision is analyzed. In the paper, the procedure of telecommunication based on data mining of Rough Sets is discussed. The factor set is...
To help handle battlefield information superiority to decision superiority (i.e. to rapidly arrive at better decisions than adversaries can respond to), many scientific, technical and technological challenges must be addressed. The most critical of those are information fusion and management at different levels, communication. This paper decribes battlefield information as data streams and mining...
One of the major concepts in business analytics is to identify the anomalies over time, also called as trend analysis. This can be easily done in pivot tables by using time as one of the dimensions, usually across columns. However, the trending information itself is insufficient to make any quick and insightful observations. Ranking the time series to identify the similar units of information can...
Association rule mining discovers interesting association or correlations among a large set of data items. Association rule mining make decision making process easier by providing the important information to the user. The most of the work done in this field is concerned with mining in an isolated form, but it is not only the case and many times more than one parties are involved in this process....
Recent progress in grid computing concept and its allied application technologies has helped us to device an efficient tool for heavy workload management. Concept of this middleware technology supports to build more and more complex application which can process large real time data set. Mobilization of under utilized resources among the needy processes can easily be carried out with distributed grid...
In the last years, the area of Multicriteria Decision Analysis (MCDA) has brought about new methods to cope with classification problems, among which those based on the concept of prototypes. These refer to specific alternatives (samples) of the training dataset that are good representatives of the groups they fit in. In this paper, experiments are conducted over two prototype selection (PS) techniques...
Combining classifiers are nowadays one of the most promising direction in pattern recognition. There are many methods of decision making which could be used by the ensemble of classifiers. The most popular are methods that have their origin in voting, where the decision of the common classifier is a combination of individual classifiers' outputs, i.e. classifiers' responses (class numbers) or values...
Multi criteria decision making (MCDM) methods help decision makers to make preference decision over the available alternatives. Evaluation and selection of the software packages is multi criteria decision making problem. Analytical hierarchy process (AHP) and weighted scoring method (WSM) have widely been used for evaluation and selection of the software packages. Hybrid knowledge based system (HKBS)...
A model of the bee hive that clearly separates the self-organizing decision-making behaviour of the bees in the hive and the problem-specific behaviour of the bees outside the hive is presented. This separation allows for the applications of the model for different problem domains. Results of the application to three problem domains are presented - web search, function optimization and hierarchical...
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