A Data Centric Privacy Preserved Mining Model for Business Intelligence Applications

In present day competitive scenario, the techniques such as data warehouse and on-line analytical process (OLAP) have become a very significant approach for decision support in data centric applications and industries. In fact the decision support mechanism puts certain moderately varied needs on database technology as compared to OLAP based applications. Data centric decision support schemes (DSS) like data warehouse might play a significant role in extracting details from various areas and for standardizing data throughout the organization to achieve a singular way of detail presentation. Such efficient data presentation facilitates information for decision making in business intelligence (BI) applications in various industrial services. In order to enhance the effectiveness and robust computation in BI applications, the optimization in data mining and its processing is must. On the other hand, being in a multiuser scenario, the security of data on warehouse is also a critical issue, which is not explored till date. In this paper a data centric and service oriented privacy preserved model for BI applications has been proposed. The optimization in data mining has been accomplished by means of C5.0 classification algorithm and comparative study has been done with C4.5 algorithm. The implementation of enhanced C5.0 algorithm with BI model would provide much better performance with privacy preservation facility for Business Intelligence applications

Space time pixels

This paper reports the design of a networked system, the aim of which is to provide an intermediate virtual space that will establish a connection and support interaction between multiple participants in two distant physical spaces. The intention of the project is to explore the potential of the digital space to generate original social relationships between people that their current (spatial or social) position can difficultly allow the establishment of innovative connections. Furthermore, to explore if digital space can sustain, in time, low-level connections like these, by balancing between the two contradicting needs of communication and anonymity. The generated intermediate digital space is a dynamic reactive environment where time and space information of two physical places is superimposed to create a complex common ground where interaction can take place. It is a system that provides awareness of activity in a distant space through an abstract mutable virtual environment, which can be perceived in several different ways – varying from a simple dynamic background image to a common public space in the junction of two private spaces or to a fully opened window to the other space – according to the participants will. The thesis is that the creation of an intermediary environment that operates as an activity abstraction filter between several users, and selectively communicates information, could give significance to the ambient data that people unconsciously transmit to others when co-existing. It can therefore generate a new layer of connections and original interactivity patterns; in contrary to a straight-forward direct real video and sound system, that although it is functionally more feasible, it preserves the existing social constraints that limit interaction into predefined patterns

Measuring Visual Complexity of Cluster-Based Visualizations

Handling visual complexity is a challenging problem in visualization owing to the subjectiveness of its definition and the difficulty in devising generalizable quantitative metrics. In this paper we address this challenge by measuring the visual complexity of two common forms of cluster-based visualizations: scatter plots and parallel coordinatess. We conceptualize visual complexity as a form of visual uncertainty, which is a measure of the degree of difficulty for humans to interpret a visual representation correctly. We propose an algorithm for estimating visual complexity for the aforementioned visualizations using Allen's interval algebra. We first establish a set of primitive 2-cluster cases in scatter plots and another set for parallel coordinatess based on symmetric isomorphism. We confirm that both are the minimal sets and verify the correctness of their members computationally. We score the uncertainty of each primitive case based on its topological properties, including the existence of overlapping regions, splitting regions and meeting points or edges. We compare a few optional scoring schemes against a set of subjective scores by humans, and identify the one that is the most consistent with the subjective scores. Finally, we extend the 2-cluster measure to k-cluster measure as a general purpose estimator of visual complexity for these two forms of cluster-based visualization

An MDA approach for developing Secure OLAP applications: metamodels and transformations

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services).This research is part of the following projects: SIGMA-CC (TIN2012-36904), GEODAS-BC (TIN2012-37493-C01) and GEODAS-BI (TIN2012-37493-C03) funded by the Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER. SERENIDAD (PEII11-037-7035) and MOTERO (PEII11- 0399-9449) funded by the Consejería de Educación, Ciencia y Cultura de la Junta de Comunidades de Castilla La Mancha, and Fondo Europeo de Desarrollo Regional FEDER

An MDA approach for developing secure OLAP applications: Metamodels and transformations

Decision makers query enterprise information stored in DataWarehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services). © 2015 ComSIS Consortium. All rights reserved.This research is part of the following projects: SIGMA-CC (TIN2012-36904), GEODAS-BC (TIN2012-37493-C01) and GEODAS-BI (TIN2012-37493-C03) funded by the Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER