Search

Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In this environment, the channel bandwidth of each channel is variant with time in real cases. However, traditional schemes do not consider time-variant bandwidth of each channel to schedule data items. Therefore, the above drawback degrades the performance in generating broadcast programs. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels of time-variant bandwidth. In view of the characteristics of time-variant bandwidth, we propose an algorithm using adaptive allocation on time-variant bandwidth to generate the broadcast program to avoid the above drawback to minimize average waiting time. Experimental results show that our approach is able to generate the broadcast programs with high quality and is very efficient in a data broadcasting environment with the time-variant bandwidth [ABSTRACT FROM AUTHOR]

Due to the dynamic nature of data streams, a sliding window is used to generate synopses that approximate the most recent data within the retrospective horizon to answer queries or discover patterns. In this paper, we propose a dynamic scheme for wavelet synopses management in sensor networks. We define a data structure Sliding Dual Tree, abbreviated as SDT, to generate dynamic synopses that adapts to the insertions and deletions in the most recent sliding window. By exploiting the properties of Haar wavelet transform, we develop several operations to incrementally maintain SDT over consecutive time windows in a time- and space- efficient manner. These operations directly operate on the transformed time-frequency domain without the need of storing/ reconstructing the original data. As shown in our thorough analysis, our SDT-based approach greatly reduces the required resources for synopses generation and maximizes the storage utilization of wavelet synopses in terms of the window length and quality measures. We also show that the approximation error of the dynamic wavelet synopses, i.e., L²-norm error, can be incrementally updated. We also derive the bound of the overestimation of the approximation error due to the incremental thresholding scheme. Furthermore, the synopses can be used to answer various kinds of numerical queries such as point and distance queries. In addition, we show that our SDT can adapt to resource allocation to further enhance the overall storage utilization overtime. As demonstrated by our experimental results, our proposed framework can outperform current techniques in both real and synthetic data. [ABSTRACT FROM AUTHOR]

Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, the identification of dense regions in previous works lacks of considering a critical problem, called "the density divergence problem" in this paper, which refers to the phenomenon that the region densities vary in different subspace cardinalities. Without considering this problem, previous works utilize a density threshold to discover the dense regions in all subspaces, which incurs the serious loss of clustering accuracy (either recall or precision of the resulting clusters) in different subspace cardinalities. To tackle the density divergence problem, in this paper, we devise a novel subspace clustering model to discover the clusters based on the relative region densities in the subspaces, where the clusters are regarded as regions whose densities are relatively high as compared to the region densities in a subspace. Based on this idea, different density thresholds are adaptively determined to discover the clusters in different subspace cardinalities. Due to the infeasibility of applying previous techniques in this novel clustering model, we also devise an innovative algorithm, referred to as DENCOS (DENsity COnscious Subspace clustering), to adopt a divide-and-conquer scheme to efficiently discover clusters satisfying different density thresholds in different subspace cardinalities. As validated by our extensive experiments on various data sets, DENCOS can discover the clusters in all subspaces with high quality, and the efficiency of DENCOS outperformes previous works. [ABSTRACT FROM AUTHOR]

In this paper, we first study an important but unsolved dilemma in the literature of subspace clustering, which is referred to as "information overlapping-data coverage" challenge. Current solutions of subspace clustering usually invoke a grid-based Apriori-like procedure to identify dense regions and construct subspace clusters afterward. Due to the nature of monotonicity property in Apriori- like procedures, it is inherent that if a region is identified as dense, all its projected regions are also identified as dense, causing overlapping/redundant clustering information to be inevitably reported to users when generating clusters from such highly correlated regions. However, naive methods to filter redundant clusters will incur a challenging problem in the other side of the dilemma, called the "data coverage" issue. Note that two clusters may have highly correlated dense regions but their data members could be highly different to each other. Arbitrarily removing one of them may lose the coverage of data with clustering information, thus likely reporting an incomplete and biased clustering result. In this paper, therefore, we further propose an innovative algorithm, called "NOnRedundant Subspace Cluster mining" (NORSC), to efficiently discover a succinct collection of subspace clusters while also maintaining the required degree of data coverage. NORSC not only avoids generating the redundant clusters with most of the contained data covered by higher dimensional clusters to resolve the information overlapping problem but also limits the information loss to cope with the data coverage problem. As shown by our experimental results, NORSC is very effective in identifying a concise and small set of subspace clusters, while incurring time complexity in orders of magnitude better than that of previous works. [ABSTRACT FROM AUTHOR]

Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points that are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain. In this paper, a mechanism named MAximal Resemblance Data Labeling (abbreviated as MARDL) is proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering representative, namely, N-Nodeset Importance Representative(abbreviated as NNIR), which represents clusters by the importance of the combinations of attribute values. MARDL has two advantages: 1) MARDL exhibits high execution efficiency, and 2) MARDL can achieve high intracluster similarity and low intercluster similarity, which are regarded as the most important properties of clusters, thus benefiting the analysis of cluster behaviors. MARDL is empirically validated on real and synthetic data sets and is shown to be significantly more efficient than prior works while attaining results of high quality. [ABSTRACT FROM AUTHOR]

Effective distance functions in high-dimensional data space, such as those used for clustering, nearest neighbor search, and indexing, are very important in solutions for many data mining problems. Recent research has shown that if the Pearson variation of the distance distribution converges to zero with increasing dimensionality, the distance function will become unstable (or meaningless) in high-dimensional space, even with the commonly used Lp, metric in the euclidean space. However, the necessary condition for unstability of a distance function, which is required for function design, remains unknown. In this paper, we shall prove that the following conditions are equivalent to unstability: 1) the Pearson variation of the distance distribution for any given query converges to 0 with increasing dimensionality, 2) the ratio of the distances of any two points to th query converges in probability to 1 with increasing dimensionality, and 3) the second moment coefficient converges to 1 with increasing dimensionality. With these results, we have the necessary and the sufficient conditions for unstability, whose negatives imply the sufficient and necessary conditions for stability. Based on these theoretical results, we employ some effective and valid indices for testing the stability of a distance function. In addition, this theoretical analysis inspires us that unstable phenomena are rooted in variation of the distance distribution. To demonstrate the theoretical results, we design a meaningful distance function, called the Shrinkage-Divergence Proximity (SDP), based on a given distance function. It is shown empirically that the SDP significantly outperforms other measures in terms of stability in high-dimensional data space, and is thus more suitable for distance-based applications. [ABSTRACT FROM AUTHOR]

The scalability of data broadcasting has been manifested by prior studies on the base of the traditional data management systems where data objects, mapped to a pair of state and value in the database, are independent, persistent, and static against simple queries. However, many modern information applications spread dynamic data objects and process complex queries for retrieving multiple data objects. Particularly, the information servers dynamically generate data objects that are dependent and can be associated into a complete response against complex queries. Accordingly, the study in this paper considers the problem of scheduling dynamic broadcast data objects in a clients-providers-servers system from the standpoint of data association, dependency, and dynamics. Since the data broadcast problem is NP-hard, we derive the lower and the upper bounds of the mean service access time. In light of the theoretical analyses, we further devise a deterministic algorithm with several gain measure functions for the approximation of schedule optimization. The experimental results show that the proposed algorithm is able to generate a dynamic broadcast schedule and also minimize the mean service access time to the extent of being very close to the theoretical optimum. [ABSTRACT FROM AUTHOR]

The K-Nearest Neighbors (KNN) query has been of significant interest in many studies and has become one of the most important spatial queries in mobile sensor networks. Applications of KNN queries may include vehicle navigation, wildlife social discovery, and squad/platoon searching on the battlefields. Current approaches to KNN search in mobile sensor networks require a certain kind of indexing support. This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes. Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use. In this paper, we propose a maintenance-free itinerary-based approach called Density-aware Itinerary KNN query processing (DIKNN). The DIKNN divides the search area into multiple cone-shape areas centered at the query point. It then performs a query dissemination and response collection itinerary in each of the cone-shape areas in parallel. The design of the DIKNN scheme takes into account several challenging issues such as the trade-off between degree of parallelism and network interference on query response time, and the dynamic adjustment of the search radius (in terms of number of hops) according to spatial irregularity or mobility of sensor nodes. To optimize the performance of DIKNN, a detailed analytical model is derived that automatically determines the most suitable degree of parallelism under various network conditions. This model is validated by extensive simulations. The simulation results show that DIKNN yields substantially better performance and scalability over previous work, both as k increases and as the sensor node mobility increases. It outperforms the second runner with up to a 50 percent saving in energy consumption and up to a 40 percent reduction in query response time, while rendering the same level of query result accuracy. [ABSTRACT FROM AUTHOR]

Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points which are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain. In this paper, a mechanism named MAximal Resemblance Data Labeling (abbreviated as MARDL) is proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering representative, namely, N-Nodeset Importance Representative (abbreviated as NNIR), which represents clusters by the importance of the combinations of attribute values. MARDL has two advantages: 1) MARDL exhibits high execution efficiency and 2) MARDL can achieve high intracluster similarity and low intercluster similarity, which are regarded as the most important properties of clusters, thus benefiting the analysis of cluster behaviors. MARDL is empirically validated on real and synthetic data sets, and is shown to be significantly more efficient than prior works while attaining results of high quality. [ABSTRACT FROM AUTHOR]

Although there have been many recent studies on the mining of sequential patterns in a static database and in a database with increasing data, these works, in general, do not fully explore the effect of deleting old data from the sequences in the database. When sequential patterns are generated; the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. Even worse, the obsolete sequential patterns that are not frequent recently may stay in the reported results. In practice, users are usually more interested in the recent data than the old ones. To capture the dynamic nature of data addition and deletion, we propose a general model of sequential pattern mining with a progressive database while the data in the database may be static, inserted, or deleted. In addition, we present a progressive algorithm Pisa, which stands for Progressive mining of Sequential pAtterns, to progressively discover sequential patterns in defined time period of interest (POI). The POI is a sliding window continuously advancing as the time goes by. Pisa utilizes a progressive sequential tree to efficiently maintain the latest data sequences, discover the complete set of up-to-date sequential patterns, and delete obsolete data and patterns accordingly. The height of the sequential pattern tree proposed is bounded by the length of POI, thereby effectively limiting the memory space required by Pisa that is significantly smaller than the memory needed by the alternative method, Direct Appending (DirApp). Note that the sequential pattern mining with a static database and with an incremental database are special cases of the progressive sequential pattern mining. By changing Start time and End time of the POI, Pisa can easily deal with a static database or an incremental database as well. Complexity of algorithms proposed is analyzed. The experimental results show that Pisa not only significantly outperforms the prior methods in execution time by orders of magnitude but also possesses graceful scalability. [ABSTRACT FROM AUTHOR]

In this paper, we leverage network coding (NC) to reduce the bandwidth consumption for data broadcast (DB). We use the concept of mixing in a different way from the traditional NC. Traditional NC mixes all data items together. However, we mix each data item from only some data items according to the queried and stored data of receivers. Therefore, each receiver in our approach is required to receive fewer coded data to decode the required information, and the sender, thereby, is able to broadcast fewer data items. We formulate an optimization problem with integer-linear programming to minimize the bandwidth consumption in DB. We prove that the problem is NP-hard and design an approximation algorithm that can be implemented in the data server. In addition, we show that different mixings of data items lead to different decoding costs for receivers. We design an algorithm to optimally code the data items with the minimum decoding cost. [ABSTRACT FROM AUTHOR]

Generally speaking, to implement Apriori-based association rule mining in hardware, one has to load candidate iten~sets and a database into the hardware. Since the capacity of the hardware architecture is fixed, if the number of candidate itemsets dr the number of items in the database is larger than the hardware capacity, the items are loaded into the hardware separately. The time complexity of those steps that need to load candidate itemsets or database items into the hardware is in proportion to the number of candidate itemsets multiplied by the number of items in the database. Too many candidate itemsets and a large database would create a performance bottleneck. In this paper, we propose a HAsh-based and PiPellned (abbreviated as HAPPI) architecture for hardware- enhanced association rule mining. We apply the pipeline methodology in the HAPPI architecture to compare itemsets with the database and collect useful information for reducing the number of candidate itemsets and items in the database simultaneou~ly. When the database is fed into the hardware, candidate itemsets are ~ompared with the items in the database to find frequent itemsets. At the same time, trimming information is collected from each transaction. In addition, itemsets are generated from transactions and hashed into a hash table. The useful trimming information and the hash table enable us to reduce the number of items in the database and the number of candidate itemsets. Therefore, we can effectively reduce the frequency of loading the database into the hardware. As such, HAPPI solves the bottleneck problem in a priori-based hardware schemes. We also derive some properties to investigate the performance of this hardware implementation. As shown by the experiment results, HAPPI significantly outperforms the previous hardware approach and the software algorithm in terms of execution time. [ABSTRACT FROM AUTHOR]

Data cubes have become important components in most data warehouse systems and decision support systems. In such systems, users usually pose very complex queries to the Online Analytical Processing (OLAP) system, and systems usually have to deal with a huge amount of data because of the large dimensionality of the sets; thus, approximating query processing has emerged as a viable solution. Specifically, the applications of cube streams handle multidimensional data sets in a continuous manner in contrast to the traditional cube approximation. Such an application collects data events for cube streams online, generates snapshots with limited resources, and keeps the approximated information in a synopsis memory for further analysis. Compared to the OLAP applications, applications of cube streams are subject to many more resource constraints on both the processing time and the memory and cannot be dealt with by existing methods due to the limited resources. In this paper, we propose the DAWA algorithm, which is a hybrid algorithm of Discrete Cosine Transform (DCI) for Data and the discrete wavelet transform (DWT), to approximate cube streams. Our algorithm combines the advantages of the high compression rate of DWT and the low memory cost of DCT. Consequently, DAWA requires much smaller working buffer and outperforms both DWT-based and DCT-based methods in execution efficiency. Also, it is shown that DAWA provides a good solution for an approximate query processing of cube streams with a small working buffer and a short execution time. The optimality of the DAWA algorithm is theoretically proved and empirically demonstrated by our experiments. [ABSTRACT FROM AUTHOR]

In recent years, data broadcasting has become a promising technique to design a mobile information system with power conservation, high scalability, and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items that should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUltiLevel Service) for an information system. There are two primary stages in MULS: online scheduling (OLS) and optimization procedure. In the first stage, we propose an OLS algorithm to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure, called Sampling with Controlled Iteration (SCI), to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (that is, the average access time of mobile users) and efficiency (that is, the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure Sd, the proposed MULS framework is able to generate broadcast programs with the flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform OLS with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS. [ABSTRACT FROM AUTHOR]

Due to the resource limitation in the data stream environments, it has been reported that answering user queries according to the wavelet synopsis of a stream is an essential ability of a Data Stream Management System (DSMS). In the literature, recent research has been elaborated upon minimizing the local error metric of an individual stream. However, many emergent applications such as stock marketing and sensor detection also call for the need of recording multiple streams in a commercial DSMS. As shown in our thorough analysis and experimental studies, minimizing global error in multiple-stream environments leads to good reliability for DSMS to answer the queries. In contrast, only minimizing local error may lead to a significant loss of query accuracy. As such, we first study in this paper the problem of maintaining the wavelet coefficients of multiple streams within collective memory so that the predetermined global error metric is minimized. Moreover, we also examine a promising application in the multistream environment, that is, the queries for top-κ range sum. We resolve the problem of efficient top-κ query processing with minimized global error by developing a general framework. For the purposes of maintaining the wavelet coefficients and processing top-κ queries, several well- designed algorithms are utilized to optimize the performance of each primary component of this general framework. We also evaluate the proposed algorithms empirically on real and simulated data streams and show that our framework can process top-κ queries accurately and efficiently. [ABSTRACT FROM AUTHOR]

In applications of multiple data streams such as stock market trading and sensor network data analysis, the clusters of streams change at different times because of data evolution. The information about evolving cluster is valuable to support corresponding online decisions. In this paper, we present a framework for Clustering Over Multiple Evolving sTreams by CORrelations and Events, which, abbreviated as COMET-CORE, monitors the distribution of clusters over multiple data streams based on their correlation. Instead of directly clustering the multiple data streams periodically, COMET-CORE applies efficient cluster split and merge processes only when significant cluster evolution happens. Accordingly, we devise an event detection mechanism to signal the cluster adjustments. The coming streams are smoothed as sequences of end points by employing piecewise linear approximation. At the time when end points are generated, weighted correlations between streams are updated. End points are good indicators of significant change in streams, and this is a main cause of a cluster evolution event. When an event occurs, through split and merge operations we can report the latest clustering results. As shown in our experimental studies, COMET-CORE can be performed effectively with good clustering quality. [ABSTRACT FROM AUTHOR]

We explore in this paper a novel sampling algorithm, referred to as algorithm PAS (standing for Proportion Approximation Sampling), to generate a high-quality online sample with the desired sample rate. The sampling quality refers to the consistency between the population proportion and the sample proportion of each categorical value in the database. Note that the state-of-the-art sampling algorithm to preserve the sampling quality has to examine the population proportion of each categorical value in a pilot sample a priori and is thus not applicable to incremental mining applications. To remedy this, algorithm PAS adaptively determines the inclusion probability of each incoming tuple in such a way that the sampling quality can be sequentially preserved while also guaranteeing the sample rate close to the user specified one. Importantly, PAS not only guarantees the proportion consistency of each categorical value but also excellently preserves the proportion consistency of multivariate statistics, which will be significantly beneficial to various data mining applications. For better execution efficiency, we further devise an algorithm, called algorithm EQAS (standing for Efficient Quality-Aware Sampling), which integrates PAS and random sampling to provide the flexibility of striking a compromise between the sampling quality and the sampling efficiency. As validated in experimental results on real and synthetic data, algorithm PAS can stably provide high-quality samples with corresponding computational overhead, whereas algorithm EQAS can flexibly generate samples with the desired balance between sampling quality and sampling efficiency. In addition, while applying the sample generated by algorithms PAS and EQAS to incremental mining applications, a significant efficiency improvement can be obtained without compromising the resulting precision, showing the prominent advantage of both proposed algorithms to be the quality-aware sampling means for incremental mining applications. [ABSTRACT FROM AUTHOR]

In the data stream environment, the patterns generated at different time instances are different due to data evolution. As time progresses, the behavior and members of clusters usually change. Hence, clustering continuous data streams allows us to observe the changes of group behavior. In order to support flexible clustering requirements, we devise in this paper a Clustering on Demand framework, abbreviated as COD framework, to dynamically cluster multiple data streams. While providing a general framework of clustering on multiple data streams, the COD framework has two advantageous features, namely, one data scan for online statistics collection and compact multiresolution approximations, which are designed to address, respectively, the time and the space constraints in a data stream environment. The COD framework consists of two phases, i.e., the online maintenance phase and the offline clustering phase. The online maintenance phase provides an efficient mechanism to maintain summary hierarchies of data streams with multiple resolutions in time linear in both the number of streams and the number of data points in each stream. On the other hand, an adaptive clustering algorithm is devised for the offline phase to retrieve approximations of desired substreams from summary hierarchies according to clustering queries. We propose two summarization techniques, based on wavelet and regression analyses, to construct the summary hierarchies. The regression-based summary hierarchy approximates the data stream more precisely and provides better clustering results, at the cost of slightly longer time than and twice the storage space as the wavelet-based one. An adaptive version of COD framework is designed to make a selection between a wavelet-based model and a regression- based model for building the summary hierarchy. By the adaptive COD, we can obtain clustering results with almost the same quality as the regression-based COD while using much less storage space for the summary hierarchy. As shown in the complexity analyses and also validated by our empirical studies, the COD framework performs very efficiently in the data stream environment while producing clustering results of very high quality. [ABSTRACT FROM AUTHOR]

Spatial clustering has attracted a lot of research attention due to its various applications. In most conventional clustering problems, the similarity measurement mainly takes the geometric attributes into consideration. However, in many real applications, the nongeometric attributes are what users are concerned about. In the conventional spatial clustering, the input data set is partitioned into several compact regions and data points which are similar to one another in their nongeometric attributes may be scattered over different regions, thus making the corresponding objective difficult to achieve. To remedy this, we propose and explore in this paper a new clustering problem on two domains, called dual clustering, where one domain refers to the optimization domain and the other refers to the constraint domain. Attributes on the optimization domain are those involved in the optimization of the objective function, while those on the constraint domain specify the application dependent constraints. Our goal is to optimize the objective function in the optimization domain while satisfying the constraint specified in the constraint domain. We devise an efficient and effective algorithm, named Interlaced Clustering-Classification, abbreviated as ICC, to solve this problem. The proposed ICC algorithm combines the information in both domains and iteratively performs a clustering algorithm on the optimization domain and also a classifications algorithm on the constraint domain to reach the target clustering effectively. The time and space complexities of the ICC algorithm are formally analyzed. Several experiments are conducted to provide the insights into the dual clustering problem and the proposed algorithm. [ABSTRACT FROM AUTHOR]

Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment, and as a consequence, the results are of limited applicability to the upcoming mobile environments. In view of this, we relax this restriction and explore in this paper the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to broadcast programs of very high quality. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of mining the informative structure of a news Web site that consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by these TOC pages. Based on the Hyperlink Induced Topics Search (HITS) algorithm, we propose an entropy-based analysis (LAMIS) mechanism for analyzing the entropy of anchor texts and links to eliminate the redundancy of the hyperlinked structure so that the complex structure of a Web site can be distilled. However, to increase the value and the accessibility of pages, most of the content sites tend to publish their pages with intrasite redundant information, such as navigation panels, advertisements, copy announcements, etc. To further eliminate such redundancy, we propose another mechanism, called Info-Discoverer, which applies the distilled structure to identify sets of article pages. Info-Discoverer also employs the entropy information to analyze the information measures of article sets and to extract informative content blocks from these sets. Our result is useful for search engines, information agents, and crawlers to index, extract, and navigate significant information from a Web site. Experiments on several real news Web sites show that the precision and the recall of our approaches are much superior to those obtained by conventional methods in mining the informative structures of news Web sites. On the average, the augmented LAMIS leads to prominent performance improvement and increases the precision by a factor ranging from 122 to 257 percent when the desired recall falls between 0.5 and 1. In comparison with manual heuristics, the precision and the recall of Info-Discoverer are greater than 0.956. [ABSTRACT FROM AUTHOR]

In this paper, we explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication database due to the following fundamental problems, i.e., 1) lack of consideration of the exhibition period of each individual item and 2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm Progressive-Partition-Miner (abbreviated as PPM) to discover general temporal association rules in a publication database. The basic idea of PPM is to first partition the publication database in light of exhibition periods of items and then progressively accumulate the occurrence count of each candidate 2-itemset; based on the intrinsic partitioning characteristics. Algorithm PPM is also designed to employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. The feature that the number of candidate 2-itemsets generated by PPM is very close to the number of frequent 2-itemsets allows us to employ the scan reduction technique to effectively reduce the number of database scans. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by other competitive schemes that are directly extended from existing methods. The correctness of PPM is proven and some of its theoretical properties are derived. Sensitivity analysis of various parameters is conducted to provide many insights into Algorithm PPM. [ABSTRACT FROM AUTHOR]

In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality-of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. As can be seen later, the former is useful for the allocation of read-intensive data objects, whereas the latter is good for the allocation of update-intensive data objects. The data allocation schemes for shared data, which are able to achieve local optimization and global optimization, are also developed. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the knowledge obtained from the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile computing system. [ABSTRACT FROM AUTHOR]

Energy saving is one of the most important issues in wireless mobile computing. Among others, one viable approach to achieving energy saving is to use an indexed data organization to broadcast data over wireless channels to mobile units. Using indexed broadcasting mobile units can be guided to the data of interest efficiently and only need to be actively listening to the broadcasting channel when the relevant information is present. In this paper, we explore the issue of indexing data with skewed access for sequential broadcasting in wireless mobile computing. We first propose methods to build index trees based on access frequencies of data records. To minimize the average cost of index probes, we consider two cases: one for fixed index fanouts and the other for variant index fanouts, and devise algorithms to construct index trees for both cases. We show that the cost of index probes can be minimized not only by employing an imbalanced index tree that is designed in accordance with data access skew, but also by exploiting variant fanouts for index nodes. Note that, even for the same index tree different broadcasting orders of data records will lead to different average data access times. To address this issue, we develop an algorithm to determine the optimal order for sequential data broadcasting to minimize the average data access time. Performance evaluation on the algorithms proposed is conducted. Examples and remarks are given to illustrate our results. [ABSTRACT FROM AUTHOR]