Narrow Results

Human knowledge about monitoring process variables is usually incomplete. To deal with this partial knowledge many types of representation other than the quantitative one are used to describe process variables (qualitative, symbolic interval). Thus, the development of automatic reasoning mechanisms about the process is faced with this problem of multiple data representations. In this paper, a unified principle for reasoning about heterogeneous data is introduced. This principle is based on a simultaneous mapping of data from initially heterogeneous spaces into only one homogeneous space based on a relative measure using appropriate characteristic functions. Once the heterogeneous data are represented in a unified space, a single processing for various analysis purposes can be performed using simple reasoning mechanisms. An application of this principle within a fuzzy logic framework is performed here to demonstrate its effectiveness. We show that simple fuzzy reasoning mechanisms can be used to reason in a unified way about heterogeneous data in three well known machine learning problems.

InfoSleuth is an agent-based system that can be configured to perform many different information management activities in a distributed environment. InfoSleuth^TM agents provide a number of complex query services that require resolving ontology-based queries over dynamically changing, distributed, heterogeneous resources. These include distributed query processing, location-independent single-resource updates, event and information monitoring, statistical or inferential data analysis, and trend discovery in complex event streams. It has been used in numerous applications, including the Environmental Data Exchange Network and the Competitive Intelligence System.

Grand Challenges in Biodiversity Informatics.

Using Biodiversity Information Effectively.

Nowadays, teaching and learning activities in a course are greatly supported by information technologies. Forums are among information technologies utilized in a course to encourage students to communicate with lecturers more outside a traditional class. Free-styled textual posts in those communications express the problems that the students are facing as well as the interest and activeness of the students with respect to each topic of a course. Exploiting such textual data in a course forum for course-level student prediction is considered in our work. Due to hierarchical structures in course forum texts, we propose a solution in this paper which combines a deep convolutional neural network (CNN) and a loss function to extract the features from textual data in such a manner that more correct recognitions of instances of the minority class which includes students with failure can be supported. In addition, other numeric data are examined and used for the task so that all the students with and without posts can be predicted in the task. Therefore, our work is the first one that defines and solves this prediction task with heterogeneous educational data at the course level as compared to the existing works. In the proposed solution, Random Forests are suggested as an effective ensemble model suitable for our heterogeneous data when many single prediction models which are random trees can be built for many various subspaces with different random features in a supervised learning process. Experimental results in an empirical evaluation on two real datasets show that a heterogeneous combination of textual and numeric data with a Random Forest model can enhance the effectiveness of our solution to the task. The best accuracy and $F$-measure values can be obtained for early predictions of the students with either success or failure. Such better predictions can help both students and lecturers beware of students’ study and support them in time for ultimate success in a course.

The era of big data brings both opportunities and challenges to scientific information analysis (SIA) and intelligent information services performed at the National Science Library, Chinese Academy of Sciences (NSLC). We are in urgent need of developing a new SIA framework to expedite big data acquisition and processing, and to improve the quality of information services. This paper describes the traditional SIA workflow currently applied at NSLC with a case study. It also reviews progresses on massive heterogeneous data integration, data management and analytics methods, and their applications. We then propose an integration-based conceptual framework for SIA through an examination of the limitations of current workflow. The new framework is characterized with the development of a Knowledge Resources Integration System (KRIS) that can store, organize, process, and visualize heterogeneous data. We explain the functions and characteristics of the proposed framework and strategies to implement a web-based data warehouse system based on it. The paper concludes with a discussion of future research to implement KRIS for SIA.

This article introduces the system architecture of big data security and the idea of realizing active security. In this article, the heterogeneous data fusion problem of big data security is solved by using the method of that converted into text-based form with text description of heterogeneous data structure. The big data mining technology, which is related to the data set, the modeling and algorithm of various intrusion patterns to calculate the probability, mining security risk points, early warning security incident occurred, is discussed. It proposed that big data security is to data acquisition clean up intermediate form of transformation to a text data stream to impact alignment of the security model, weighted arithmetic judged, risk model of the monitoring and intrusion early warning. Looking forward for the future of big data security development of intelligent active security.