Intelligent data analysis - support for development of SMEs sector

The paper studies possibilities of intelligent data analysis application for discovering knowledge hidden in small and medium-sized enterprises’ (SMEs) data, on the territory of the province of Vojvodina. The knowledge revealed by intelligent analysis, and not accessible by any other means, could be the valuable starting point for working out of proactive and preventive actions for the development of the SMEs sector.Intelligent data analysis, CRISP-DM, clustering, small and medium enterprises., Research and Development/Tech Change/Emerging Technologies, C8, L2,

Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

Financial benchmarking of transportation companies in the New York Stock Exchange (NYSE) through data envelopment analysis (DEA) and visualization

In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution

A list of websites and reading materials on strategy & complexity

The list has been developed based on a broad interpretation of the subject of ‘strategy & complexity’. Resources will therefore more, or less directly relate to ‘being strategic in the face of complexity’. Many of the articles and reports referred to in the attached bibliography can be accessed and downloaded from the internet. Most books can be found at amazon.com where you will often find a number of book reviews and summaries as well. Sometimes, reading the reviews will suffice and will give you the essence of the contents of the book after which you do not need to buy it. If the book looks interesting enough, buying options are easy

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

How Does the Visualization of Data Change How it is Interpreted?

Big data is one of the most promising trends in technology and business today. Big data refers to large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially relating to human behavior and interactions. Big data sets hold valuable information with the potential to improve efficiency in the workplace by giving us insight into various areas. How can we extract information from data? Visualizations and aggregations are frequently used to represent data in a manageable way. The construction of these tools requires usage of design principles to leverage human ability to translate data into knowledge that can be used to support decisions. Our project creates and executes a survey to discover whether participants vary in their ability to draw conclusions from data presented in aggregate formats. In this paper, we are focused on whether one visualization is more interpretable than the others. We do not go into the details of variation between people on any factor (e.g. education, personality, or other characteristics). Based on our results, we will suggest design principles for visualizations that improve the ability to comprehend data quickly

Visual Methods for Examining Support Vector Machine Results, with Applications to Gene Expression Data Analysis

Support vector machines (SVM) offer a theoretically well-founded approach to automated learning of pattern classifiers. They have been proven to give highly accurate results in complex classification problems, for example, gene expression analysis. The SVM algorithm is also quite intuitive with a few inputs to vary in the fitting process and several outputs that are interesting to study. For many data mining tasks (e.g., cancer prediction) finding classifiers with good predictive accuracy is important, but understanding the classifier is equally important. By studying the classifier outputs we may be able to produce a simpler classifier, learn which variables are the important discriminators between classes, and find the samples that are problematic to the classification. Visual methods for exploratory data analysis can help us to study the outputs and complement automated classification algorithms in data mining. We present the use of tour-based methods to plot aspects of the SVM classifier. This approach provides insights about the cluster structure in the data, the nature of boundaries between clusters, and problematic outliers. Furthermore, tours can be used to assess the variable importance. We show how visual methods can be used as a complement to cross-validation methods in order to find good SVM input parameters for a particular data set

A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application