Margaret H. Dunham

Bio: Margaret H. Dunham is an academic researcher from Southern Methodist University. The author has contributed to research in topics: Mobile computing & Cluster analysis. The author has an hindex of 22, co-authored 71 publications receiving 2129 citations. Previous affiliations of Margaret H. Dunham include University of Virginia & University of Missouri–Kansas City.

Using semantic caching to manage location dependent data in mobile computing

Abstract: Location-dependent applications are becoming very popular in mobile environments. To improve system performance and facilitate disconnection, caching is crucial to such applications. In this paper, a semantic caching scheme is used to access location dependent data in mobile computing. We first develop a mobility model to represent the moving behaviors of mobile users and formally define location dependent queries. We then investigate query processing and cache management strategies. The performance of the semantic caching scheme and its replacement strategy FAR is evaluated through a simulation study. Our results show that semantic caching is more flexible and effective for use in LDD applications than page caching, whose performance is quite sensitive to the database physical organization. We also notice that the semantic cache replacement strategy FAR, which utilizes the semantic locality in terms of locations, performs robustly under different kinds of workloads.

A mobile transaction model that captures both the data and movement behavior

Abstract: Unlike distributed transactions, mobile transactions do not originate and end at the same site. The implication of the movement of such transactions is that classical atomicity, concurrency and recovery solutions must be revisited to capture the movement behavior. As an effort in this direction, we define a model of mobile transactions by building on the concepts of split transactions and global transactions in a multidatabase environment. Our view of mobile transactions, called Kangaroo Transactions, incorporates the property that transactions in a mobile computing system hop from one base station to another as the mobile unit moves through cells. Our model is the first to capture this movement behavior as well as the data behavior which reflects the access to data located in databases throughout the static network. The mobile behavior is dynamic and is realized in our model via the use of split operations. The data access behavior is captured by using the idea of global and local transactions in a multidatabase system.

Semantic caching and query processing

Abstract: Semantic caching is very attractive for use in distributed systems due to the reduced network traffic and the improved response time. It is particularly efficient for a mobile computing environment, where the bandwidth of wireless links is a major performance bottleneck. Previous work either does not provide a formal semantic caching model, or lacks efficient query processing strategies. This paper extends the existing research in three ways: formal definitions associated with semantic caching are presented, query processing strategies are investigated and, finally, the performance of the semantic cache model is examined through a detailed simulation study.

Mobile computing and databases: anything new?

Abstract: Recent advances in hardware technologies, such as portable computers and wireless communication networks, have led to the emergence of mobile computing systems. No one challenges the idea that mobile computing offers many opportunities for research within the Computer Science area. However, one could ask are there really any new database problems introduced when a mobile computing environment is assumed. We feel that the status of data management in mobile computing is similar to that of distributed data management versus centralized data management in the late 60s. Namely that many of the issues are the same, but the solutions are different. This analogy has been raised by others [1, 12]. We use this as the basis by which we answer the above question. We concentrate on discussing the differences between data management solutions in a mobile computing environment and those in a distributed database environment. The purpose of this paper is twofold: to spawn further interest in mobile computing research and to convince the skeptics that there are new research topics in mobile computing worthy of further examination.

Location dependent query processing

Abstract: The advances in wireless and mobile computing allow a mobile user to perform a wide range of aplications once limited to non-mobile hard wired computing environments As the geographical position of a mobile user is becoming more trackable, users need to pull data which are related to their location, perhaps seeking information about unfamiliar places or local lifestyle data In these requests, a location attribute has to be identified in order to provide more efficient access to location dependent data, whose value is determined by the location to which it is related Local yellow pages, local events, and weather information are some of the examples of these dataIn this paper, we give a formalization of location relatedness in queries We differentiate location dependence and location awareness and provide thorough examples to support our approach

Scalable algorithms for association mining

Abstract: Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. We present efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The items are organized into a subset lattice search space, which is decomposed into small independent chunks or sublattices, which can be solved in memory. Efficient lattice traversal techniques are presented which quickly identify all the long frequent itemsets and their subsets if required. We also present the effect of using different database layout schemes combined with the proposed decomposition and traversal techniques. We experimentally compare the new algorithms against the previous approaches, obtaining improvements of more than an order of magnitude for our test databases.

MAFIA: A maximal frequent itemset algorithm for transactional databases

Abstract: We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very long. The search strategy of our algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms. Our implementation of the search strategy combines a vertical bitmap representation of the database with an efficient relative bitmap compression schema. In a thorough experimental analysis of our algorithm on real data, we isolate the effect of the individual components of the algorithm. Our performance numbers show that our algorithm outperforms previous work by a factor of three to five.

Fast vertical mining using diffsets

Abstract: A number of vertical mining algorithms have been proposed recently for association mining, which have shown to be very effective and usually outperform horizontal approaches. The main advantage of the vertical format is support for fast frequency counting via intersection operations on transaction ids (tids) and automatic pruning of irrelevant data. The main problem with these approaches is when intermediate results of vertical tid lists become too large for memory, thus affecting the algorithm scalability.In this paper we present a novel vertical data representation called Diffset, that only keeps track of differences in the tids of a candidate pattern from its generating frequent patterns. We show that diffsets drastically cut down the size of memory required to store intermediate results. We show how diffsets, when incorporated into previous vertical mining methods, increase the performance significantly.

Data stream clustering: A survey

Abstract: Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.

MAFIA: a maximal frequent itemset algorithm for transactional databases

Abstract: We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very long. The search strategy of our algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms. Our implementation of the search strategy combines a vertical bitmap representation of the database with an efficient relative bitmap compression schema. In a thorough experimental analysis of our algorithm on real data, we isolate the effect of the individual components of the algorithm. Our performance numbers show that our algorithm outperforms previous work by a factor of three to five.