How Registries Can Help Performance Measurement Improve Care

Suggests ways to better utilize databases of clinical information to evaluate care processes and outcomes and improve measurements of healthcare quality and costs, comparative clinical effectiveness research, and medical product safety surveillance

First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

International audienceThe development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques

SLIM : Scalable Linkage of Mobility Data

We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of location based services, or producing a unified dataset from multiple sources for urban planning. Such integrated datasets are also essential for service providers to optimise their services and improve business intelligence. In this paper, we first propose a mobility based representation and similarity computation for entities. An efficient matching process is then developed to identify the final linked pairs, with an automated mechanism to decide when to stop the linkage. We scale the process with a locality-sensitive hashing (LSH) based approach that significantly reduces candidate pairs for matching. To realize the effectiveness and efficiency of our techniques in practice, we introduce an algorithm called SLIM. In the experimental evaluation, SLIM outperforms the two existing state-of-the-art approaches in terms of precision and recall. Moreover, the LSH-based approach brings two to four orders of magnitude speedup

User Generated Content In Social Media As A Source For Assessing Cultural Dimensions

This research in progress intends to document the process of mining social media text-based content in order to acquire measures of cultural dimensions. This process can potentially be replicated and extended to other topics of inquiry so as to provide researchers with an alternative source for data acquisition related to measuring theoretical constructs. Additionally, we provide an argument supporting the credibility of user generated content (UGC) as a data source for rigorous inquiry. This article describes the mechanism for acquiring pertinent UGC as well as describing the method of assessing and quantifying the valence and magnitude of the various construct values. These measures are intended to be suitable for utilization across a variety of methods for statistical analysis requiring continuous or discrete factor measures. We conclude with a discussion of limitations and benefits and intend to present preliminary results during the conference