46076168
Adopting IT for Effective Management of Social Welfare Programs
In North Carolina, welfare programs are state supervised and county administered. To properly manage welfare programs in such an environment, efficient timely communication of a comprehensive set of data analysis customized to each county is required between the state and the 100 counties in North Carolina. Consequently a dynamic website that tracks the experiences of families and individuals on Work First by county was built to assist county DSS (Departments of Social Services) program managers in evaluating their local program. The information technology not only provides a means for efficient dissemination of information, but also shows social workers a new way of thinking by presenting complex information in a user friendly manner. The information provided through the website allows many counties to evaluate their performance in terms of outcomes. It has provided a common set of measures that can be compared over time and across counties.
2548187
Mining asynchronous periodic patterns in time series data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min rep and max dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space
7408751
Quantitative Association Analysis Using Tree Hierarchies
Association analysis arises in many important applications such as bioinformatics and business intelligence. Given a large collection of measurements over a set of samples, association analysis aims to find dependencies of target variables to subsets of measurements. Most previous algorithms adopt a two-stage approach; they first group samples based on the similarity in the subset of measurements, and then they examine the association between these groups and the specified target variables without considering the inter-group similarities or alternative groupings. This can lead to cases where the strength of association depends significantly on arbitrary clustering choices. In this paper, we propose a tree-based method for quantitative association analysis. Tree hierarchies derived from sample similarities represent many possible sample groupings. They also provide a natural way to incorporate domain knowledge such as ontologies and to identify and remove outliers. Given a tree hierarchy, our association analysis evaluates all possible groupings and selects the one with strongest association to the target variable. We introduce an efficient algorithm, TreeQA, to systematically explore the search-space of all possible groupings in a set of input trees, with integrated permutation tests. Experimental results show that TreeQA is able to handlelarge-scale association analysis very efficiently and is more effective and robust in association analysis than previous methods.
5788516
Efficient genome ancestry inference in complex pedigrees with inbreeding
Motivation: High-density SNP data of model animal resources provides opportunities for fine-resolution genetic variation studies. These genetic resources are generated through a variety of breeding schemes that involve multiple generations of matings derived from a set of founder animals. In this article, we investigate the problem of inferring the most probable ancestry of resulting genotypes, given a set of founder genotypes. Due to computational difficulty, existing methods either handle only small pedigree data or disregard the pedigree structure. However, large pedigrees of model animal resources often contain repetitive substructures that can be utilized in accelerating computation. Results: We present an accurate and efficient method that can accept complex pedigrees with inbreeding in inferring genome ancestry. Inbreeding is a commonly used process in generating genetically diverse and reproducible animals. It is often carried out for many generations and can account for most of the computational complexity in real-world model animal pedigrees. Our method builds a hidden Markov model that derives the ancestry probabilities through inbreeding process without explicit modeling in every generation. The ancestry inference is accurate and fast, independent of the number of generations, for model animal resources such as the Collaborative Cross (CC). Experiments on both simulated and real CC data demonstrate that our method offers comparable accuracy to those methods that build an explicit model of the entire pedigree, but much better scalability with respect to the pedigree size. Contact: weiwang@cs.unc.edu
14969897
Dynamic adaptive file management in a local area network
In light of advances in processor and networking technology, especially the emergence of network attached disks, the traditional client-server architecture of file systems has become suboptimal for many computation/data intensive applications. We introduce a revised architecture for file management employing network attached storage: the dynamic file server environment (Dynamo). Dynamo introduces two main architectural innovations: to provide high scalability, the file management functions are mainly performed cooperatively by the clients in the system. Furthermore, data is transferred directly to the client's cache from network-attached disks, thus avoiding copies from a disk to the server buffer and then over the network to the client. Secondly, Dynamo uses a cooperative cache management which employs a decentralized lottery-based page replacement strategy. We show via performance benchmarks run on the Dynamo system and simulation results how this architecture increases the system's adaptability, scalability and cost performance.
62615149
Mining Asynchronous Periodic Patterns in Time Series Data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min/spl I.bar/rep and max/spl I.bar/dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space efficiency.
207162632
Utility-based anonymization for privacy preservation with less information loss
Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often used for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.
 In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
14325893
Maintaining K-Anonymity against Incremental Updates
K-anonymity is a simple yet practical mechanismto protect privacy against attacks of re-identifying individuals by joining multiple public data sources. All existing methods achieving k-anonymity assume implicitly that the data objects to be anonymized are given once and fixed. However, in many applications, the real world data sources are dynamic. In this paper, we investigate the problem of maintaining k-anonymity against incremental updates, and propose a simple yet effective solution. We analyze how inferences from multiple releases may temper the k-anonymity of data, and propose the monotonic incremental anonymization property. The general idea is to progressively and consistently reduce the generalization granularity as incremental updates arrive. Our new approach guarantees the k-anonymity on each release, and also on the inferred table using multiple releases. At the same time, our new approach utilizes the more and more accumulated data to reduce the information loss.
17247210
Identities on Bell polynomials and Sheffer sequences
In this paper, we study exponential partial Bell polynomials and Sheffer sequences. Two new characterizations of Sheffer sequences are presented, which indicate the relations between Sheffer sequences and Riordan arrays. Several general identities involving Bell polynomials and Sheffer sequences are established, which reduce to some elegant identities for associated sequences and cross sequences.
16034501
Effective Keyword Search in XML Documents Based on MIU
Keyword search is an effective approach for most users to search for information because they do not need to learn complex query languages or the underlying structures of the data. This paper focuses on effective keyword search in XML documents which are modeled as labeled trees. We first analyze the problems caused by the refinement of result granularity during XML keyword search and then propose to partition an XML document into XML fragments with the granularity of Minimal Information Unit (MIU). Furthermore, we present efficient index structures and the corresponding search algorithms. Finally, our comprehensive experiments demonstrate the benefits of our method over previously proposed methods in terms of result quality, index size and execution time.
10807505
Refining Web authoritative resource by frequent structures
The Web resource is a rich collection of the dynamic information, which is useful in various disciplines. There has also been much research work related to improving the quality of information searching in the Web. However, most of the work is still inadequate to satisfy a diversified demand from users. In this paper, we exploit the hyperlinks in the Web and propose a new approach called SFP in order to improve the quality of research results obtain from search engines. The SFP algorithm evolves from the frequent pattern mining technique, which is a common data mining technique for conventional databases. The essential idea of our approach is to mine the frequent structures of links from a given Web topology. By using the SFP algorithm, we extract the authoritative pages and communities from the complex Web topology. We demonstrate our approach by running several experiments and show that the performance and functionalities of using the SFP in managing search results are better than other known methods such as HITS.