15025287
An Ontology Based Approach to Construct Behaviors in Web Information Systems
System behaviors specify the major functions of domain specific Web Information Systems (WIS). Traditional techniques can not satisfy various requirements or manage innumerous data while developing WIS behaviors. People appeal to a smart tool for implementing the WIS behaviors. This article makes the following contributions: (1) Proposes the concept of domain ontology and behavior ontology to describe the contents and operations of WIS; (2) Extends traditional ECA model to characterize the triggers, parameters, actions as well as validations of WIS behaviors; (3) Analyses the relationships between domain ontology and behavior ontology with rule sets; (4) Implements a tool named WISE Builder with four algorithms to help users building behavior ontology with domain ontology; (5)Shows the feasibility of this technique in a real application case.
1558162
Monitoring Abnormal Patterns with Complex Semantics over ICU Data Streams
Monitoring abnormal patterns in data streams is an important research area for many applications. In this paper we present a new approach MAPS(Monitoring Abnormal Patterns over data Streams) to model and identify the abnormal patterns over the massive data streams. Compared with other data streams, ICU streaming data have their own features: pseudo-periodicity and polymorphism. MAPS first extracts patterns from the online arriving data streams and then normalizes them according to their pseudo-periodic semantics. Abnormal patterns will be detected if they are satisfied the predicates defined in the clinician-specifying normal patterns. At last, a real application demonstrates that MAPS is efficient and effective in several important aspects.
9175149
Generation of Comprehensible Hypotheses from Gene Expression Data
Machine learning techniques have been recognized as powerful tools for the analysis of gene expression data. However, most learning techniques used in class prediction in gene expression analysis during the past years generate black-box models. Although the prediction accuracy of these models could be very well, they provide little insight into the biological facts. This paper holds the recognition that a more reasonable role for machine learning techniques is to generate hypotheses that can be verified or refined by human experts instead of making decisions for human experts. Based on this recognition, a general approach to generate comprehensible hypotheses from gene expression data is described and applied to human acute leukemias as a test case. The results demonstrate the feasibility of using machine learning techniques to help form hypotheses on the relationship between genes and certain diseases.
5393650
PaDDMAS: parallel and distributed data mining application suite
Discovering complex associations, anomalies and patterns in distributed data sets is gaining popularity in a range of scientific, medical and business applications. Various algorithms are employed to perform data analysis within a domain, and range from statistical to machine learning and AI based techniques. Several issues need to be addressed however to scale such approaches to large data sets, particularly when these are applied to data distributed at various sites. As new analysis techniques are identified, the core tool set must enable easy integration of such analytical components. Similarly, results from an analysis engines must be sharable, to enable storage, visualisation or further analysis of results. We describe the architecture of PaDDMAS, a component based system for developing distributed data mining applications. PaDDMAS provides a tool set for combining pre-developed or custom components using a dataflow approach, with components performing analysis, data extraction or data management and translation. Each component is wrapped as a Java/CORBA object, and has an interface defined in XML. Components can be serial or parallel objects, and may be binary or contain a more complex internal structure. We demonstrate a prototype using a neural network analysis algorithm.
879315
Kolmogorov Random Graphs and the Incompressibility Method
We investigate topological, combinatorial, statistical, and enumeration properties of finite graphs with high Kolmogorov complexity (almost all graphs) using the novel incompressibility method. Example results are (i) the mean and variance of the number of (possibly overlapping) ordered labeled subgraphs of a labeled graph as a function of its randomness deficiency (how far it falls short of the maximum possible Kolmogorov complexity) and (ii) a new elementary proof for the number of unlabeled graphs.
368077
Approximation of Walrasian equilibrium in single-minded auctions
We consider a social optimization model of pricing scheme in single-minded auctions, in cases where Walrasian equilibrium does not exist. We are interested in the maximization of the ratio, R, of happy bidders over all agents, in a feasible allocation-pricing scheme. We show NP-hardness of the optimization problem, establish lower and upper bounds of R, as well as develop greedy algorithms to approximate the optimal value of R.
16510993
Rational quadratic approximation to real algebraic curves
An algorithm is proposed to give a global approximation to an implicit real plane algebraic curve with rational quadratic B-splines. The algorithm consists of three steps: curve segmentation, segment approximation and curve tracing. The curve is first divided into so-called triangle convex segments. Then each segment is approximated with several rational quadratic Bezier curves. At last, the curve segments are connected into several maximal branches and each branch is represented by a B-spline curve resulting in a C/sup 1/ global parameterization for the curve branch. Due to the detailed geometric analysis, high accuracy of approximation may be achieved with a small number of quadratic segments. The final approximation based on quadratic spline curves keeps many important geometric features and gives a refined topological structure of the original curve.