Attribute Value Reordering For Efficient Hybrid OLAP

The normalization of a data cube is the ordering of the attribute values. For large multidimensional arrays where dense and sparse chunks are stored differently, proper normalization can lead to improved storage efficiency. We show that it is NP-hard to compute an optimal normalization even for 1x3 chunks, although we find an exact algorithm for 1x2 chunks. When dimensions are nearly statistically independent, we show that dimension-wise attribute frequency sorting is an optimal normalization and takes time O(d n log(n)) for data cubes of size n^d. When dimensions are not independent, we propose and evaluate several heuristics. The hybrid OLAP (HOLAP) storage mechanism is already 19%-30% more efficient than ROLAP, but normalization can improve it further by 9%-13% for a total gain of 29%-44% over ROLAP

Discourse network analysis: policy debates as dynamic networks

Political discourse is the verbal interaction between political actors. Political actors make normative claims about policies conditional on each other. This renders discourse a dynamic network phenomenon. Accordingly, the structure and dynamics of policy debates can be analyzed with a combination of content analysis and dynamic network analysis. After annotating statements of actors in text sources, networks can be created from these structured data, such as congruence or conflict networks at the actor or concept level, affiliation networks of actors and concept stances, and longitudinal versions of these networks. The resulting network data reveal important properties of a debate, such as the structure of advocacy coalitions or discourse coalitions, polarization and consensus formation, and underlying endogenous processes like popularity, reciprocity, or social balance. The added value of discourse network analysis over survey-based policy network research is that policy processes can be analyzed from a longitudinal perspective. Inferential techniques for understanding the micro-level processes governing political discourse are being developed

The Session Inventory Module within an Intelligent Tutoring System for Data Normalization

The knowledge acquisition of data normalization has been commonly perceived as a hurdle that is challenging students in the beginning database classes. As an effort to provide students with additional assistance while learning this topic, an intelligent tutoring system is proposed and implemented that can be used as a virtual private tutor to teach students in a one-on-one manner. This paper describes the strategically design and management of the tutorial sessions within this system. These sessions are designed and maintained according to their theme topics and difficulty levels so that the virtual tutor can dynamically select sessions based on the assessment of a student’s knowledge level and progress

Efficient K-Mean Clustering Algorithm for Large Datasets using Data Mining Standard Score Normalization

In this paper, the clustering and data mining techniques has been introduced. The data mining is useful for extract the useful information from the large database/dataset. For extract the information with efficient factor, the data mining Normalization techniques can be used. These techniques are Min-Max, Z-Scaling and decimal Scaling normalization. Mining of data becomes essential thing for easy searching of data with normalization. This paper has been proposed the efficient K-Mean Clustering algorithm which generates the cluster in less time. Cluster Analysis seeks to identify homogeneous groups of objects based on the values of their attribute. The Z-Score normalization technique has been used with Clustering concept. The number of large records dataset has been generated and has been considered for analyze the results. The existing algorithm has been analyzed by WEKA Tool and proposed algorithm has been implemented in C#.net. The results have been analyzed by generating the timing comparison graphs and proposed works shows the efficiency in terms of time and calculatio

The parameterized space complexity of model-checking bounded variable first-order logic

The parameterized model-checking problem for a class of first-order sentences (queries) asks to decide whether a given sentence from the class holds true in a given relational structure (database); the parameter is the length of the sentence. We study the parameterized space complexity of the model-checking problem for queries with a bounded number of variables. For each bound on the quantifier alternation rank the problem becomes complete for the corresponding level of what we call the tree hierarchy, a hierarchy of parameterized complexity classes defined via space bounded alternating machines between parameterized logarithmic space and fixed-parameter tractable time. We observe that a parameterized logarithmic space model-checker for existential bounded variable queries would allow to improve Savitch's classical simulation of nondeterministic logarithmic space in deterministic space $O(\log^2n)$. Further, we define a highly space efficient model-checker for queries with a bounded number of variables and bounded quantifier alternation rank. We study its optimality under the assumption that Savitch's Theorem is optimal