Due to the widespread availability of GPS devices, a large amount of trajectory data can be collected. These data come in the form of unbounded streams, called trajectory streams. Many applications like urban planning, transportation, and ecology benefit from performing spatial queries on these data, but due to the Big Data size, spatial indexes need to be built to process queries efficiently. Existing systems for trajectory stream data management and analytics do not exploit the online, continuous behavior of trajectory streams for indexing, leading to wasted time. In this paper, we introduce PIMMLI, a Spark-based trajectory stream query processing algorithm that avoids re-indexing spatial data with the arrival of each new batch of points by forecasting the trajectories, expanding their Minimum Bounding Rectangles accordingly. Our experiments on a real-life dataset show that PIMMLI has comparable query execution time with competitors while having up to 29% savings in indexing time.

The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.

During the last decade, big data management has attracted increasing interest from both the industrial and academic communities. In parallel, Cyber Security has become mandatory due to various and more intensive threats. In June 2022, a group of researchers has met to reflect on their community’s impacts on current research challenges. In particular, they have considered four dimensions: (1) dedicated systems being data processing and analytic platforms or time series management systems; (2) graphs analytics and distributed computation; (3) privacy; and (4) new hardware.

The Second International Workshop on Big Data in Emergent Distributed Environments (BiDEDE) focuses on scalable data management issues in emergent computing environments like (post) cloud and fog/edge/dew computing. All these computing environments aim to smoothly integrate scalable data management and processing into distributed environments, such that communication and computational costs are reduced for higher throughput, lower latencies of applications and extending battery lifetimes of nodes in companion with robust approaches to overcome failures and crashes. While there has been research in these areas for already over one decade, still many open challenges exist because of technology triggers like lightweight virtualization, increasing capabilities of nodes and increasing massive parallelization. This workshop supports lively discussions in these and related areas.

A data stream is an infinite sequence of data points generated from a source continuously at a fast rate, which is characterized by the transiency of the data points, the temporal relationship among the data points, concept drift, and multi-dimensionality of data points. Outlier detection in data streams thus needs to deal with the characteristics of Big Data applications such as volume, velocity, and variety. The problem of detecting outliers in multiple concurrent data streams introduces additional challenges to the problem. In this paper, we propose a parallel outlier detection technique CODS to detect Contextual Outliers in multiple concurrent independent multi-dimensional Data Streams using a Graphics Processing Unit (GPU). The proposed algorithm addresses all the aforesaid characteristics of data streams. A set of experiments demonstrates reasonable outlier detection accuracy and scalability of CODS with the number of data streams.

Cloud database providers provision different architectures to guarantee high availability. One of these architectures is a cluster database that consists of several database engine nodes, where data is replicated among the nodes. Although the cloud database providers provide various auto-indexing tools, these tools mostly address characteristics of a database deployed on a single node, not a cluster. It is possible to install an index advisor on each node, which recommends an index set for that node. The problem with this approach is that the current index advisors for a single node aim to minimize the processing cost of the workload; however, on a cluster database, other goals such as load balancing can be considered. Hence, the better solution could be an index advisor which has a comprehensive view of the cluster node.

Data mining techniques can be used in several significant types of research in various domains to understand the flows of data, discover hidden knowledge, and improve quality of life. Sequential pattern mining, which is one data mining method, has emerged in the transportation domain to recognize the dynamic behavior of vehicles, trains, people...etc. The idea of sequential pattern mining is to find the frequent subsequences in a database of sequences. Although multiple sequential pattern mining techniques can mine sequential patterns, timestamps are only used to order sequences and time between sequence events is ignored. This information is important in real applications, such as traffic recommendation system and transportation safety. Though knowing that measurement Y occurs after measurement X is valuable, it is more valuable to know the estimated time before the appearance of measurement Y, perhaps, for example, to schedule maintenance at the right time in order to prevent railway damages. In this paper, we propose an algorithm called Minits (MINIng Timed Sequential patterns) to find the frequent sequential patterns and include the transition time between events in these patterns. However, approaches that depend on serial architecture are not effective anymore due to the massive data that are frequently generated from different sources. Therefore, we exploit parallelism using multicore CPUs to improve the performance of Minits to handle big data. Extensive experiments on real and synthetic datasets are reported and show the significance and advantages of this approach. Also, the execution time using a multicore outperform the single core when it deals with big data.

DBSCAN is a density-based clustering algorithm that is especially useful for finding clusters of arbitrary shapes. As opposed to other clustering techniques, like K-means, it does not require the number of clusters to be specified as an input parameter, and it is highly robust to outliers. However, DBSCAN has a worst-case quadratic time complexity, which makes it difficult to handle large dataset sizes. To address this problem, several works have been proposed that exploit the massive parallelism of GPUs in DBSCAN clustering. Nonetheless, none of these works have been experimentally compared against each other. In this paper, we review the existing GPU algorithms for DBSCAN clustering and conduct the first experimental study comparing these GPU algorithms using three real-world datasets to identify the best performing algorithm. Our results show that CUDA-DClust is the best performing GPU algorithm in terms of execution time and memory requirements.

The International Workshop on Semantic Big Data (SBD) is already in the fourth year in conjunction with the ACM SIGMOD Conference. By focusing on topics related to Semantic Web and Big Data, it highlights these important research areas to all participants of the SBD workshop and the main conference. Hence, the workshop offers an ideal forum for open discussions and establishment of professional relationships between academic researchers and industrial members of the Semantic Web and database communities.

Nowadays, location-aware devices, such as GPS, provide a huge volume of spatial-temporal data. Analyzing the data to understand the behavior of objects (e.g. people) could be beneficial in many application areas. Due to the spatial and temporal nature and their complexity, researchers have developed various data mining techniques such as trajectory segmentation, which splits the trajectories into sub-trajectories, to prepare them for the mining step. A central issue in discovering knowledge is choosing an appropriate trajectory segmentation technique. In this paper, we provide a comparative study on two trajectory segmentation techniques, density-based and grid-based, when applied to sequential patterns discovery. We conducted experiments using two real-life datasets to evaluate the performance of the methods in terms of execution time and their impact on discovering the sequential patterns. The experimental results showed that the density-based is more efficient, while the grid-based is more effective.

With the increasing prevalence of location sensor devices like GPS, it has been possible to collect large datasets of a special type of spatio-temporal data called trajectory data. A trajectory is a discrete sequence of positions that a moving object occupies in space as time passes. Such large datasets enable researchers to study the behavior of the objects describing these movements by issuing spatial queries. Among the queries that can be issued are top-K trajectory similarity queries, which retrieve the K most similar trajectories to a given query trajectory. This query has applications in many areas, such as urban planning, ecology and social networking; however, this query is computationally expensive. In this work, we introduce a new parallel top-K trajectory similarity query technique for GPUs, FastTopK, to deal with these challenges. Our experiments on two large real-life datasets showed that FastTopK produces on average 107.96X smaller candidate result sets, and 3.36X faster query execution times than the existing state-of-the-art technique, TKSimGPU.

Most of the existing database query optimization techniques are designed to target traditional database systems with one-dimensional optimization objectives. These techniques usually aim to reduce either the query response time or the I/O cost of a query. Evidently, these optimization algorithms are not suitable for cloud database systems because they are provided to users as on-demand services which charge for their usage. In this case, users will take both query response time and monetary cost paid to the cloud service providers into consideration for selecting a database system product. Thus, query optimization for cloud database systems needs to target reducing monetary cost in addition to query response time. This means that query optimization has multiple objectives which are more challenging than one-dimensional objectives found in traditional paradigms. Similar problems exist when incorporating query re-optimization into the query execution process to obtain more accurate, multi-objective cost estimates. This paper presents a query optimization method that achieves two goals: 1) identifying a query execution plan that satisfies the multiple objectives provided by the user and 2) reducing the costs of running the query execution plan by performing adaptive query re-optimization during query execution. The experimental results show that the proposed method can save either the time cost or the monetary cost based on the type of queries.

Data streams are sequences of data points that have the properties of transiency, infiniteness, concept drift, uncertainty, multi-dimensionality, cross-correlation among different streams, asynchronous arrival, and heterogeneity. In this paper we propose a new outlier detection technique for multiple multi-dimensional data streams, called Wadjet, that addresses all the issues of outlier detection in multiple data streams. Wadjet exploits the temporal correlations to identify outliers in each individual data stream, and after this, it exploits the cross-correlations between data streams to identify points that do not conform with these cross-correlations. Experiments comparing Wadjet against existing techniques on real and synthetic datasets show that Wadjet achieves 18.8× higher precision, and competitive execution time and recall.

Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms that include humans “in-the-loop” have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans “over-the-loop”, giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.

When running queries on a database, choosing an optimal query execution plan to minimize query costs is crucial for the query optimizer. This is especially true in mobile-cloud database systems, where there are multiple costs to execute a query plan such as money, time and energy. In order to fulfill different cost objectives for different users, some query optimizers allow users to select the query execution plan from a Pareto Set based on Skyline queries. The users must select from a potentially large quantity of options, and these options present the values of costs. It is not straightforward to the users how to compare these values in such a way to choose the option that suits their needs best. This increases the possibility for users to choose in-optimal options, and the amount of time spent to make that choice. However, the existing user interaction model during multi-objective query processing is unable to solve this issue. To fill this gap, this paper presents a new user interaction model in multi-objective query processing. This model introduces the administrators, or super users, to the user interaction process, allowing them to preset Weight Profiles and their logical descriptions. Weight Profiles contain objective preferences for the users before the query is executed. By using this model, the users can select a Weight Profile that will obtain their optimal query execution plan, and the process of choosing will be more accurate and efficient.

Processing large-scale data is typically memory intensive. The current generation of Graphics Processing Units (GPUs) has much lower memory capacity than CPUs which is often a limiting factor in processing large-scale data on GPUs. It is desirable to reduce memory footprint in spatially joining large-scale datasets through query optimization. In this study, we present a parallel selectivity estimation technique for optimizing spatial join processing on GPUs. By integrating the multi-dimensional cumulative histogram structure and the summed-area-table algorithm, our data parallel selectivity estimation technique can be efficiently realized on GPUs. Experiments on spatially joining two sets of Minimum Bounding Boxes (MBBs) derived from real point and polygon data, each with about one million MBBs, have shown that selectivity estimation at four grid levels took less than 1/3 of a second on a Nvidia GTX Titan GPU device. By using the best grid resolution, our technique saves 38.4% memory footprint for the spatial join.

A trajectory is a polygonal line consisting of the positions that a moving object occupies as time passes, and as such, it can be derived by periodically sampling the positions of the object. In this manner, and due to the proliferation of location-sensing devices, it has been possible to create large datasets of trajectories. Using these datasets it is possible to derive much information about the movement patterns of the objects. However, trajectory data is uncertain, and this can negatively impact the accuracy of data mining algorithms used to obtain the movement patterns of objects. One of the sources of trajectory uncertainty is the error inherent to GPS measurements, and another source relates to the fact that in practice many trajectories have sampling points that are on average too far in time, so it is difficult to determine its movement between points. In this paper, we propose a technique called TrajEstU that estimates the trajectory of a moving object in an unconstrained space. The algorithm is applied when there is uncertainty in trajectories due to measurement errors and/or low sampling rates. Experiments show that TrajEstU achieves up to 98% accuracy on real life and synthetic trajectory datasets.

Among many Big Data applications are those that deal with data streams. A data stream is a sequence of data points with timestamps that possesses the properties of transiency, infiniteness, uncertainty, concept drift, and multi-dimensionality. In this paper we propose an outlier detection technique called Orion that addresses all the characteristics of data streams. Orion looks for a projected dimension of multi-dimensional data points with the help of an evolutionary algorithm, and identifies a data point as an outlier if it resides in a low-density region in that dimension. Experiments comparing Orion with existing techniques using both real and synthetic datasets show that Orion achieves an average of 7X the precision, 5X the recall, and a competitive execution time compared to existing techniques.

Businesses are struggling to stay ahead of competition in a globalized economy where there are more and stronger competitors. Managers are constantly looking for advantages that can generate benefits at low costs. One way to have such advantage is using the data about customers, demographic data, purchase history, customer behavior and preferences that can help to take better business decisions. Data Mining addresses the challenges of collecting value inside data and the ways to put that value to use for virtually any area of our lives, including business. In this paper, we address the interest of Data Mining for business and analyze three popular Open Source Data Mining Tools – KNIME, Orange and RapidMiner – considered as a good starting point for enterprises to begin exploring the power of Data Mining and its benefits.

There exist large datasets containing the sequences of points that moving objects occupy in space as time goes by. Such sequences of moving objects are known as trajectories. Being able to issue queries that allow the extraction of patterns from the movements of these objects is important to many real world applications, such as urban planning in transportation and bird migration tracking in ecology. One example of such queries is the top-K trajectory similarity query. This type of query receives as input arguments two sets P and Q of trajectories and a positive integer k, and seeks to find for every trajectory p in P the set of k trajectories in Q that are the most similar to p. However, querying these trajectory data is both compute and I/O intensive. In this paper we explore the potential of GPGPUs for supporting, in a scalable manner, top-K trajectory similarity queries. To this end, we propose an algorithm, called TKSimGPU, that incorporates parallelization strategies in order to answer this type of trajectory queries. We conducted experiments comparing the throughput of top-K trajectory similarity queries performed on multicore CPUs and GPGPUs using a large scale real world trajectory dataset. The experiments show that TKSimGPU achieved a 3.37x speedup in query processing time over exhaustive search on a GPU, and a 4.9x speedup in query processing time on a 12-core CPU architecture.

Huge amounts of geospatial rasters, such as remotely sensed imagery and environmental modeling output, are being generated with increasingly finer spatial, temporal, spectral and thematic resolutions. In this study, we aim at developing a lightweight lossless data compression technique that balances the performance between compression and decompression for large-scale geospatial rasters. Our Bitplane bitmap Quadtree (or BQ-Tree) based technique encodes the bitmaps of raster bitplanes as compact quadtrees which can compress and index rasters simultaneously. The technique is simple by design and lightweight by implementations. Except computing Z-order codes for cache efficiency, only bit level operations are required. Extensive experiments using 36 rasters of the NASA Shuttle Range Topography Mission (SRTM) 30 meter resolution elevation data with 20 billion raster cells have shown that our BQ-Tree technique is more than 4X faster for compression and 36% faster for decompression than zlib using a single CPU core while achieving similar compression ratios. Our technique further has achieved 10-13X speedups for compression and 4X speedups for decompression using 16 CPU cores on the experiment machine equipped with dual Intel Xeon 8-core E5-2650V2 CPUs. Our technique compares favorably with the best known technique with respect to both compression and decompression throughputs.

A key factor of measuring database performance is query response time, which is dominated by I/O time. Database partitioning is among techniques that can help users reduce the I/O time significantly. However, how to efficiently partition tables in a database is not an easy problem, especially when we want to have this partitioning task done automatically by the system itself. This paper introduces an algorithm called Self-Managing Online Partitioner for Databases (SMOPD) in vertical partitioning based on closed item sets mining from a query set and system statistic information mined from system statistic views. This algorithm can dynamically monitor the database performance using user-configured parameters and automatically detect the performance trend so that it can decide when to perform a re-partitioning action without feedback from DBAs. This algorithm can free DBAs from the heavy tasks of keeping monitoring the system and struggling against the large statistic tables. The paper also presents the experimental results evaluating the performance of the algorithm using the TPC-H benchmark.

Motivated by the practical needs for efficiently processing large-scale taxi trip data, we have developed techniques for high performance online spatial, temporal and spatiotemporal aggregations. These techniques include timestamp compression to reduce memory footprint, simple linear data structures for efficient in-memory scans and utilization of massively data parallel GPU accelerations for spatial joins. Our experiments have shown that the combined performance boosting techniques are able to perform various spatial, temporal and spatiotemporal aggregations on hundreds of millions of taxi trips in the order of a few seconds using commodity personal computers equipped with multi-core CPUs and many-core GPUs. The high throughputs in a personal computing environment are encouraging in the sense that high-performance OLAP queries on large-scale data is feasible when the parallel processing power of modern commodity hardware is fully utilized which is important for interactive OLAP applications.

One of the most important metrics in measuring the performance of a database system is query response time, which is composed of I/O time and CPU time. I/O time is decided by the amount of data read/write from/to disks and how the data is located on disks. CPU time is decided by how the database system performs the query operations. So if we want to reduce the query response time we can reduce either I/O time or CPU time, or both of them. We know retrieving data from disks is much slower than retrieving data from main memory. Hence, one of the common ways to reduce I/O times is clustering data on disks so that queries will access only relevant data. This paper introduces an efficient algorithm, called AutoClust, for automatic database attribute clustering (or also called automatic database vertical partitioning) for single computers as well as cluster computers. It is based on closed item sets mined from queries and their attributes using association rule mining. The paper then presents experimental results comparing the performance of AutoClust with that of a baseline algorithm on both single computers and cluster computers using the TPC-H benchmark running on major commercial database systems. The experiments show that AutoClust has better query costs for both types of computers.

Global remote sensing and large-scale environmental modeling have generated huge amounts of raster geospatial data. While the inherent data parallelism of large-scale raster geospatial data allows straightforward coarse-grained parallelization at the chunk level on CPUs, it is largely unclear how to effectively exploit such data parallelism on massively parallel General Purpose Graphics Processing Units (GPGPUs) that require fine-grained parallelization. In this study, we have developed an efficient spatial data structure called BQ-Tree to code raster geospatial data by exploiting the uniform distributions of quadrants of bitmaps at the bitplanes of a raster. A fine-grained parallelization scheme has been implemented using Nvidia CUDA. Experiments show that the GPGPU implementation is capable of decoding a BQ-Tree encoded 16-bits NASA MODIS geospatial raster with 22,658*15,586 cells in 190 milliseconds, i.e., 1.86 billion cells per second, on an Nvidia C2050 GPU card. The performance achieves a 5.9X speedup when compared with the best dual quadcore CPU implementation and a 36.9X speedup compared with a highly optimized single core CPU implementation.

Outlier detection is a well established area of statistics but most of the existing outlier detection techniques are designed for applications where the entire dataset is available for random access. A typical outlier detection technique constructs a standard data distribution or model and identifies the deviated data points from the model as outliers. Evidently these techniques are not suitable for online data streams where the entire dataset, due to its unbounded volume, is not available for random access. Moreover, the data distribution in data streams change over time which challenges the existing outlier detection techniques that assume a constant standard data distribution for the entire dataset. In addition, data streams are characterized by uncertainty which imposes further complexity. In this paper we propose an adaptive, online outlier detection technique addressing the aforementioned characteristics of data streams, called Adaptive Outlier Detection for Data Streams (A-ODDS), which identifies outliers with respect to all the received data points as well as temporally close data points. The temporally close data points are selected based on time and change of data distribution. We also present an efficient and online implementation of the technique and a performance study showing the superiority of A-ODDS over existing techniques in terms of accuracy and execution time on a real-life dataset collected from meteorological applications.

Advances in geospatial technologies have generated large amounts of raster geospatial data. Massively parallel General Purpose Graphics Processing Unit (GPGPU) computing technologies have provided personal computers with tremendous computing capabilities. In this paper, we report our work on fast indexing of large-scale raster geospatial data using GPGPU computing. We have designed a cache conscious quadtree data structure (CCQ-Tree) that is suitable for GPU indexing. A set of algorithms have been developed and integrated to construct CCQ-Trees on GPUs by utilizing multiple pyramid data structures and Z-order based prefix sum. Experiments on multiple 4096*4096 blocks of a global precipitation raster data have shown that CCQ-Tree indexing using a 112-core Nvidia Quadro FX3700 GPU device reduces construction times from around 9.83 seconds to 0.42 seconds (23X speedup).

With the rapid growth of database applications, wireless networking technology and mobile computing devices, there is a demand for processing mobile transactions in Mobile Ad-hoc Network (MANET) databases, so that mobile users can access and manipulate data anytime and anywhere. However, in order to guarantee timely and correct results for multiple concurrent transactions, concurrency control (CC) techniques become critical. Due to the characteristics of MANET databases, existing CC algorithms cannot work effectively. In this paper, we propose a CC algorithm called Sequential Order with Dynamic Adjustment (SODA) for MANET databases. In the design of SODA, the major characteristics of MANET databases are taken into consideration. SODA is based on optimistic CC to offer high concurrency and avoid unbounded blocking time, utilizes the sequential order of committed transactions to improve response time, and dynamically adjusts the sequential order of committed transactions to reduce aborts. The simulation results confirm that SODA has lower abort rate than other existing techniques.

In this paper, we present a data mining framework to estimate missing or corrupted data in sensor network applications - a frequently occurring phenomenon in this domain. The framework is naturally germane to the spatio-temporal analysis of relational data stream evolution. Our method utilizes association rules to capture spatio-temporal correlations in multivariate, dynamically evolving, and unbounded sensor data streams. Existing approaches that tackled this problem do not account for the multi-dimensionality of the node data and their relationship; furthermore they entail simplistic and/or premature assumptions on the temporal and spatial factors to overcome the complexity of the streaming environment. Our technique, called Mining Autonomously Spatio-Temporal Environmental Rules (MASTER), comprehensively formulates the problem of mining patterns in sensor data streams, and yet remains provably adaptive to bounded time and space costs while probabilistically assuring a bounded estimation error. Simulation experiments show MASTER's efficiency in terms of overhead as well as the quality of estimation.

Species distribution data play an important role in biodiversity related research, especially in exploring relationships with the environment. In the recent years, both the number of species being explored and the spatial resolution of species distribution data are increasing fast. It is thus imperative to develop database systems that allow users to efficiently query such large-scale data based on spatial and non-spatial (e.g., taxonomic and phylogenetics) criteria.In this paper, we present our approach to building such a system by integrating several components, including a quadtree representation of binary raster data, tree path indexing and query processing in PostgreSQL, and window decomposition techniques for spatial queries. Our unique contribution is in associating species identifiers with intermediate quadtree nodes and query optimization for multiple independent queries after window query decomposition. Our system enables PostgreSQL to support binary raster data without requiring any changes to the database backend and is suitable for managing large-scale species distribution data.Our experiments using 4000+ bird species distribution data related to the Western hemisphere show that the proposed approach in associating species identifiers with quadtree nodes reduces the number of database tuples by more than 1/3 and the average identifiers to be associated with each tuple from 110.6 to 4.8, a significant improvement compared to classic quadtree-based approaches. With respect to query optimization, optimized queries are 6--9.5 times faster than the baseline queries for average query response times and 5.5--8.3 times faster than the baseline queries for maximum query response times for four query window sizes ranging from 0.1 to 5.0 degrees. Our query optimization techniques thus make the system suitable for many interactive applications for querying and exploring species distribution data.

A sensor network is a valuable new form of collective computational instrumentation by virtue of its ability to sense physical quantities of interest and to transmit such readings via sub-networks of nodes/computers for processing. Such computing environment typically generates massive amounts of data rapidly in real-time. These infinite volumes of online time-series are formally characterized as data streams. The wealth of fast incoming data streams presents both overhead and logistic challenges for sensor network applications. In this research, we introduce an online data mining framework to serve as an overhead-bounded knowledge discovery tool for sensornet applications. Our framework extends the notions of traditional association rules to multivariate continuous data and uses spatio-temporal correlations to make intelligent inferences about the monitored variables. Our mining framework is additionally pertinent to data estimation, which is an important capability given the inevitability of data loss/corruption with the current sensornet technology. Experimentation shows efficiency of our approach both in terms of overhead cost and quality of missing data estimates.

Exploratory analysis of large-scale species distribution data is essential to gain information and knowledge, stimulating hypotheses and seeking possible explanations of species distribution patterns. Geographical Information System (GIS) has played an important role in modeling and visualizing species distribution patterns for a single or a limited number of species. However, traditional GIS models do not take taxonomic components of species distribution data into consideration and are neither effective nor efficient in managing large-scale species distribution data.

In traditional fixed-wired networks, standard protocols like 2-Phase-Commit are used to guarantee atomicity for distributed transactions. However, within mobile networks, a higher probability of failures including node failures, message loss, and even network partitioning makes the use of these standard protocols difficult or even impossible. To use traditional database applications within a mobile scenario, we need an atomic commit protocol that reduces the chance of infinite blocking. In this paper, we present an atomic commit protocol called multi coordinator protocol (MCP) that uses a combination of the traditional 2-Phase-Commit, 3-Phase-Commit, and consensus protocols for mobile environments. Simulation experiments comparing MCP with 2PC show how MCP enhances stability for the coordination process by involving multiple coordinators, and that the additional time needed for the coordination among multiple coordinators is still reasonable.

A Mobile Ad-Hoc Network (MANET) is a collection of wireless autonomous mobile nodes with no fixed infrastructure. Since no fixed infrastructure is required, MANET fits well in military operations, emergency disaster rescue, and mobile ad-hoc voting. There are many issues that have to be addressed while designing a technique for managing real-time database transactions in MANET: 1) energy limitations; 2) client and server mobility; 3) real-time constraints imposed on transactions; and 4) frequent disconnection and network partitioning. We have designed PETRANET1: a Power-Efficient Transaction management technique for Real-time mobile Ad-hoc NETwork databases that addresses the above specified issues. In this paper, we present a system prototype that we have developed to implement PETRANET for a military database application.

In a Mobile Ad-hoc Network (MANET), due to the mobility and energy limitations of nodes, disconnection and network partitioning occur frequently. In addition, transactions in many MANET database applications have time constraints. In this paper, a Data REplication technique for real-time Ad-hoc Mobile databases (DREAM) that addresses all these issues is proposed. DREAM is prototyped on laptops and PDAs and compared with two existing replication techniques using a military database application.

Feature extraction techniques have been used to handle high-dimensional data and experimental studies often show improved classification accuracies. Unfortunately very few studies provide concrete evidences on the effectiveness of these feature extraction techniques and they largely remain to be black boxes. In this study, we design and implement a visualization prototype system that allows users to look into the classification processes, explore the links among the original and extracted features in different classifiers, examine why and how an instance is correctly or incorrectly classified. We demonstrate the prototype's capabilities by combining a feature extraction method based on hierarchical feature space clustering with J48 decision tree classifiers and perform experiments on a real hyperspectral remote sensing image dataset.

Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent closed itemsets, but they are mainly intended for traditional transaction databases and thus do not take data stream characteristics into consideration. In this paper, we propose a novel approach for mining closed frequent itemsets over data streams. It computes and maintains closed itemsets online and incrementally, and can output the current closed frequent itemsets in real time based on users' specified thresholds. Experimental results show that our proposed method is both time and space efficient, has good scalability as the number of transactions processed increases and adapts very rapidly to the change in data streams.

Existing XML query pattern-based caching strategies focus on extracting the set of frequently issued query pattern trees based on the number of occurrences of the query pattern trees in the history. Each occurrence of the same query pattern tree is considered equally important for the caching strategy. However, the same query pattern tree may occur at different timepoints in the history of XML queries. This temporal feature can be used to improve the caching strategy. In this paper, we propose a novel type of query pattern called conserved query paths for efficient caching by integrating the support and temporal features together. Conserved query paths are paths in query pattern trees that never change or do not change significantly most of the time (if not always) in terms of their support values during a specific time period. We proposed an algorithm to extract those conserved query paths. By ranking those conserved query paths, a dynamic-conscious caching (DCC) strategy is proposed for efficient XML query processing. Experiments show that the DCC caching strategy outperforms the existing XML query pattern tree-based caching strategies.

Existing web usage mining techniques focus only on discovering knowledge based on the statistical measures obtained from the static characteristics of web usage data. They do not consider the dynamic nature of web usage data. In this paper, we focus on discovering novel knowledge by analyzing the change patterns of historical web access sequence data. We present an algorithm called WAM-MINER to discover Web Access Motifs (WAMs). WAMs are web access patterns that never change or do not change significantly most of the time (if not always) in terms of their support values during a specific time period. WAMs are useful for many applications, such as intelligent web advertisement, web site restructuring, business intelligence, and intelligent web caching.

While traditional fixed-wired network protocols like 2-phase-commit guarantee atomicity, we cannot use them in mobile low bandwidth networks where network partitioning, node failure, and message loss may result in blocking. To deploy traditional database applications easily into a mobile environment, there is a demand for a protocol which guarantees an atomic commit of transactions. This paper introduces a protocol which can guarantee such atomic commitment in mobile environments using a combination of commit and consensus protocols. In addition, it takes advantage of mobile network sub-structures like single-hop environments to reduce message transfer costs.

Data broadcasting is well known for its excellent scalability. Multi-dimensional range queries, such as spatial range queries of geographical information for location dependent services, are very popular queries in mobile computing. Query response time is greatly affected by the order in which data items are being broadcast. This paper proposes a non-greedy, low polynomial time cost optimization method to place data over a wireless broadcast channel for multi-dimensional range query processing. Experimental results show that the method, together with proper global constraints based on application semantics, can greatly reduce total access time to the data channel. Compared with the heuristic data placement methods purely based on the access frequencies, the reduction of access time can be as much as 54%.

With the wide development of databases in general and data warehouses in particular, it is important to reduce the tasks that a database administrator must perform manually. The aim of auto-administrative systems is to administrate and adapt themselves automatically without loss (or even with a gain) in performance. The idea of using data mining techniques to extract useful knowledge for administration from the data themselves has existed for some years. However, little research has been achieved. This idea nevertheless remains a very promising approach, notably in the field of data warehousing, where queries are very heterogeneous and cannot be interpreted easily. The aim of this study is to search for a way of extracting useful knowledge from stored data themselves to automatically apply performance optimization techniques, and more particularly indexing techniques. We have designed a tool that extracts frequent itemsets from a given workload to compute an index configuration that helps optimizing data access time. The experiments we performed showed that the index configurations generated by our tool allowed performance gains of 15% to 25% on a test database and a test data warehouse.

Energy saving is the most important issue in wireless mobile computing due to power constraints on mobile units. Data broadcasting is the main method of information dissemination in wireless networks as its cost is independent of the number of mobile hosts receiving the information. A number of data broadcasting techniques have been proposed for mobile wireless networks, where servers have no energy restrictions, but little research has been done to address the issue of data broadcasting in mobile ad-hoc networks where both servers and clients are nomadic. In this paper, we propose two groups of broadcast scheduling algorithms called adaptive broadcasting and popularity based adaptive broadcasting that consider time constraints on requests as well as energy limitation on both servers and clients. We also present the simulation experiments that compare the algorithms.

Article A toggle transaction management technique for mobile multidatabases Share on Authors: Ravi A. Dirckze School of Computer Science, University of Oklahoma, Norman, Oklahoma 73019 School of Computer Science, University of Oklahoma, Norman, Oklahoma 73019View Profile , Le Gruenwald School of Computer Science, University of Oklahoma, Norman, Oklahoma 73019 School of Computer Science, University of Oklahoma, Norman, Oklahoma 73019View Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 371–377https://doi.org/10.1145/288627.288679Online:01 November 1998Publication History 15citation383DownloadsMetricsTotal Citations15Total Downloads383Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Update technique is an important issue related to database recovery. In a main memory database environment, transaction execution can be processed without any I/O, and all I/O operations involved are for recovery purposes. The efficiency of update techniques therefore has an important impact on the performance of main memory database systems. We compared the techniques of immediate and deferred update based on a database machine, MARS. The simulation results showed that immediate update outperforms deferred update unless system failure is a frequent occurrence.

article Free Access Share on MMDB reload algorithms Authors: Le Gruenwald School of Electrical Engineering and Computer Science, The University of Oklahoma, Norman, Oklahoma School of Electrical Engineering and Computer Science, The University of Oklahoma, Norman, OklahomaView Profile , Margaret H. Eich Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas Department of Computer Science and Engineering, Southern Methodist University, Dallas, TexasView Profile Authors Info & Claims ACM SIGMOD RecordVolume 20Issue 2June 1991 pp 397–405https://doi.org/10.1145/119995.115858Online:01 April 1991Publication History 34citation423DownloadsMetricsTotal Citations34Total Downloads423Last 12 Months10Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF