L-overlay: A layered data management scheme for peer-to-peer computing

With the growth of information technology and computer networks, there is a vital need for optimal design of distributed databases with the aim of performance improvement in terms of minimizing the round-trip response time and query transmission and processing costs. To address this issue, new fragmentation, data allocation, and replication techniques are required. In this paper, we propose enhanced vertical fragmentation, allocation, and replication schemes to improve the performance of distributed database systems. The proposed fragmentation scheme clusters highly-bonded attributes (i.e., normally accessed together) into a single fragment in order to minimize the query processing cost. The allocation scheme is proposed to find an optimized allocation to minimize the round-trip response time. The replication scheme partially replicates the fragments to increase the local execution of queries in a way that minimizes the cost of transmitting replicas to the sites. Experimental results show that, on average, the proposed schemes reduce the round-trip response time of queries by 23% and query processing cost by 15%, as compared to the related work.

For speeding up query processing on Big Data, frequent sub-queries or views may be materialized such that the query processing cost is minimized with optimum cost of maintaining the materialized views and/or queries. Materializing frequent sub-queries and views means that resultant data set of the views reside in the memory of one or more nodes in the cluster, so that it reduces the MapReduce cost, submission and scheduling cost of Distributed File System jobs for query processing. We have defined materialized views as resultant data of frequent sub-queries and aggregation functions of a set of Big Data warehousing queries that are saved for enhancing query performance. The problem is defined as a multi-objective optimization problem for minimizing the total query processing MapReduce cost, MapReduce cost for maintaining the materialized views and the number of views selected for materializing with maximized total size of the views selected. We applied Differential Evolution algorithm and NSGA-II to study their performances for developing a recommendation system for selecting views for materializing in Big Data warehousing.

This is the conference report of the 23 rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2015), held in Seattle, Washington, USA, November 3-6, 2015. This conference was the twenty-third edition in a series of symposia and workshops that began in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL) and provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces, and visualization to data storage, query processing, indexing and data mining.

These proceedings contain the papers from the 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2021), held online November 2-5, 2021. The conference started as a series of workshops and symposia back in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. It continues to provide a forum for original research contributions covering all conceptual, design and implementation aspects of geospatial data ranging from applications, user interfaces and visualization, to data storage, query processing, indexing and data mining. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).

The conference started as a series of workshops and symposia back in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. It continues to provide a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces and visualization, to data storage, query processing, indexing and data mining. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).

There is an increasing demand for locality-preserving distribution of complex data structures in peer-to-peer systems. Current systems either do not preserve object locality or suffer from imbalances in data distribution, routing state, and/or query processing costs. In this position paper, we take a systematic approach that enables the deployment of searchable tree structures in p2p environments. We achieve distributed tree traversal with efficient routing distance and routing state. We show how to implement several p2p applications using distributed tree structures.

In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human-computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining.

Due to the increasing availability and sophistication of data recording techniques, multiple information sources and distributed computing are becoming the important trends of modern information systems. Many applications such as security informatics and social computing require a ubiquitous data analysis platform so that decisions can be made rapidly under distributed and dynamic system environments. Although data mining has now been popularly used to achieve such goals, building a data mining system is, however, a nontrivial task, which may require a complete understanding on numerous data mining techniques as well as solid programming skills. Employing agent techniques for data analysis thus becomes increasingly important, especially for users not familiar with engineering and computational sciences, to implement an effective ubiquitous mining platform. Such data mining agents should, in practice, be intelligent, complete, and compact. In this paper, we present an interactive data mining agent — OIDM (online interactive data mining), which provides three categories (classification, association analysis, and clustering) of data mining tools, and interacts with the user to facilitate the mining process. The interactive mining is accomplished through interviewing the user about the data mining task to gain efficient and intelligent data mining control. OIDM can help users find appropriate mining algorithms, refine and compare the mining process, and finally achieve the best mining results. Such interactive data mining agent techniques provide alternative solutions to rapidly deploy data mining techniques to broader areas of data intelligence and knowledge informatics.

Unstructured peer-to-peer infrastructure has been widely employed to support large-scale distributed applications. Many of these applications, such as location-based services and multimedia content distribution, require the support of range selection queries. Under the widely adopted query shipping protocols, the cost of query processing is affected by the number of result copies or replicas in the system. Since range queries can return results that include poorly-replicated data items, the cost of these queries is usually dominated by the retrieval cost of these data items. In this work, we propose a popularity-aware prefetch-based approach that can effectively facilitate the caching of poorly-replicated data items that are potentially requested in subsequent range queries, resulting in substantial cost savings. We prove that the performance of retrieving poorly-replicated data items is guaranteed to improve under an increasing query load. Extensive experiments show that the overall range query processing cost decreases significantly under various query load settings.

In a multidatabase system, the participating databases are autonomous. The schemas of these databases may be different in various ways, while the same information is represented. A global query issued against the global database needs to be translated to a proper form before it can be executed in a local database. Since data requested by a query (or a part of a query) is sometimes available in multiple sites, the site (database) that processes the query with the least cost is the desired query processing site. The authors study the effect of differences in schemas on the cost of query processing in a multidatabase environment. They first classify schema conflicts to different types. For each type of conflict, they show how much more or less complex a translated query can become in comparison with the originally user-issued global query. Based on this observation, they propose an analytical method that considers the conflicts between local databases and finds the database(s) that renders the least execution cost in processing a global query. This research introduces a new level of query optimization (termed the schema-level optimization) in multidatabase environments. The results provide a new dimension of enhancement for the capability of a query optimizer in multidatabase systems.

Data mining is a technique for analyzing large amounts of data, in various formats, often called Big Data, in order to gain knowledge about it. The healthcare industry is the next Big Data area of interest as its large variability in patients, their health status and their records which can include image scans, graphical test results, and hand-written physician notes, has been untapped for analysis. In addition to data mining, there is a newer analysis method called process mining. Process mining is similar to data mining in that large data files are reviewed and analyzed, but in this case, event logs specific to a particular process or series of processes, are analyzed. Process mining allows one to understand the initial baseline, determine any bottlenecks or resource constraints, and evaluate a recently implemented change. Process mining was conducted on a hospital event log of patients entering the emergency room with sepsis, to better understand this newer analysis method, to highlight the information discovered, and to determine its role with data mining. Not only did the analysis of the event logs provide process mapping and process analysis, but it also highlighted areas in the clinical operations in need of further investigation, including a possible relationship with patient re-admission and their release method. In addition, the data mining method of creating a histogram, of the process data, was applied, allowing data mining and process mining to be utilized complimentary.

Skyline queries help users make intelligent decisions over complex data, where different and often conflicting criteria are considered. A challenging problem is to support skyline queries in distributed environments, where data is scattered over independent sources. The query response time of skyline processing over distributed data depends on the amount of transferred data and the query processing cost at each server. In this paper, we propose AGiDS, a framework for efficient skyline processing over distributed data. Our approach reduces significantly the amount of transferred data, by using a grid-based data summary that captures the data distribution on each server. AGiDS consists of two phases to compute the result: in the first phase the querying server gathers the grid-based summary, whereas in the second phase a skyline request is sent only to the servers that may contribute to the skyline result set asking only for the points of non-dominated regions. We provide an experimental evaluation showing that our approach performs efficiently and outperforms existing techniques.

The explosive growth of web-based information systems has created various sources and vast quantities of semi-structured data, which need to be indexed by search engines in order to allow the retrieval of documents according to user needs. However, one of the major challenges in the development of indexing techniques for semi-structured data is related to how to index not only textual but also structural content. The main issue is how to efficiently handle branching path expressions without introducing precision loss as well as undesired increase of query processing costs and index file sizes. Several proposals for indexing semi-structured data can be found in the literature. Despite their relevant contributions, existing proposals suffer from at least one of the problems related to precision loss, storage space requirements and query processing costs. In such a context, this paper proposes an efficient, lossless path-based indexing technique for semi-structured data, which deals with a well-defined class of branching path expressions, preserving one-to-many relationships among elements. As evinced by experimental evaluation, the adoption of the proposed technique results in excellent query processing time and generates smaller index file sizes than structural join indexing techniques.

In order to facilitate efficient query processing, the information contained in data warehouses is typically stored as a set of materialized views. Deciding which views to materialize represent a challenge in order to minimize view maintenance and query processing costs. Some existing approaches are applicable only for small problems, which are far from reality. In this paper we introduce a new approach for materialized view selection using Parallel Simulated Annealing (PSA) that selects views from an input Multiple View Processing Plan (MVPP). With PSA, we are able to perform view selection on MVPPs having hundreds of queries and thousands of views. Also, in our experimental study we show that our method provides a significant improvement in the quality of the obtained set of materialized views over existing heuristic and sequential simulated annealing algorithms.

연속된 일차원 실수로 이루어진 시계열 데이터는 데이터 마이닝이나 데이터 웨어하우징과 같은 다양한 데이터베이스 응용 분야에서 연구되어져 왔다. 그러나 최근의 복잡한 비즈니스 환경에서, 다차원 데이터 시퀀스(multidimensional data sequence : MDS)는 일차원 시계열 데이터와 더불어 그 중요성이 더해가고 있다. 다차원 데이터 시퀀스의 예로써, 비디오 스트림은 색상과 질감 등의 속성들로 이루어진 다차원 공간상에서 MDS로 나타낼 수 있다. 본 논문에서는 패턴 유사성 검색에서 사용되는 효과적인 유사성 척도를 제시한다. 하나의 MDS는 여러 개의 세그먼트(segment)로 나누어지며, 각 세그먼트는 다양한 의미적인 특징들로 표현된다. 유사성 척도는 이러한 세그먼트에 대해서 정의되는데 이 척도를 사용하여 어떤 주어진 질의 시퀀스에 대하여 무관한 세그먼트들은 검색 대상에서 일차적으로 제외된다. 데이터 시퀀스와 질의 시퀀스 모두 세그먼트 단위로 분할되며, 질의 처리는 전체 시퀀스의 모든 데이터를 검색하지 않고 데이터 세그먼트와 질의 세그먼트의 특징을 비교하는 것을 기초로 하여 수행된다. One-dimensional time-series data have been studied in various database applications such as data mining and data warehousing. However, in the current complex business environment, multidimensional data sequences (MDS') become increasingly important in addition to one-dimensional time-series data. For example, a video stream can be modeled as an MDS in the multidimensional space with respect to color and texture attributes. In this paper, we propose the effective similarity measures on which the similar pattern retrieval is based. An MDS is partitioned into segments, each of which is represented by various geometric and semantic features. The similarity measures are defined on the basis of these segments. Using the measures, irrelevant segments are pruned from a database with respect to a given query. Both data sequences and query sequences are partitioned into segments, and the query processing is based upon the comparison of the features between data and query segments, instead of scanning all data elements of entire sequences.