Search

Stored procedures are an important feature of all major database systems that allows to execute application logic within database servers. This paper reports on experiences to implement a popular scientific algorithm, the Basic Local Alignment Search Tool (BLAST), as stored procedures within a relational database. We implemented the un-gapped, nucleotide version of the BLAST algorithm with four different relational database engines, both commercial and open source. In an experimental evaluation, we compared our dbBLAST implementations with a standard file-based BLAST implementation from NCBI with regard to the implementation effort, runtime performance, and scalability. It shows that although our dbBLAST runs faster than the file-based BLAST program for short query sequences, all implementations lack scalability. However, the results also indicate that stored procedures require significant less development effort—both in time and space—than traditional programming approaches. [ABSTRACT FROM AUTHOR]

The DTD of a set of XML documents may change due to many reasons such as changes to the real world events, changes to the user's requirements, and mistakes in the initial design. In this paper, we present a novel algorithm called DTD-Diff to detect the changes to DTDs that defines the structure of a set of XML documents. Such change detection tool can be useful in several ways such as maintenance of XML documents, incremental maintenance of relational schema for storing XML data, and XML schema integration. We compare DTD-Diff with existing XML change detection approaches and show that converting DTD to XML Schema (XSD) (which is in XML document format) and detecting the changes using existing XML change detection algorithms is not a feasible option. Our experimental results show that DTD-Diff is 5-325 times faster than X-Diff when it detects the changes to the XSD files. We also study the result quality of detected deltas. [ABSTRACT FROM AUTHOR]

Efficient full-text keyword search remains a challenging problem in P2P systems. Most of the traditional keyword search systems on DHT overlay networks perform the join operation of keywords at document level, which consumes a huge amount of storage and bandwidth. In this paper, we present KEYNOTE, a novel keyword search system that performs the join operation at node level. Compared to the traditional keyword search systems on DHTs, KEYNOTE can greatly reduce the storage and communication cost. To forward a query to the relevant nodes for searching documents, two effective node selection methods are presented. To address the hot spot problem in Chord overlay networks, an efficient load balancing scheme is introduced. Simulated experimental evaluation with up to 8,000 nodes and over 600,000 real-world documents validates the practicality of the proposed system. [ABSTRACT FROM AUTHOR]

Sybase ASE (Adaptive Server Enterprise) is a cost based database system. Statistics information plays a key role in the costing model of ASE optimizer. Typically, up-to-date statistics is critical in selecting an optimal query plan with good performance. However, updating statistics is a resource intensive maintenance operation. A common user concern is the lack of input on when statistics needs to be updated and also the time taken to maintain the statistics. In this paper, we introduce a new solution for automating statistics maintenance in Sybase ASE 15.0. Our solution includes a new metric for evaluating data changes due to DMLs (Data Management Language), the use of a scheduler to generate rules to gather statistics based on feedback from the metric and random sampling of data when gathering statistics. This approach will make statistics maintenance more intelligent and efficient, and reduce the TCO (Total Cost of Ownership) significantly. [ABSTRACT FROM AUTHOR]

A structural join evaluates structural relationship (parent-child or ancestor-descendant) between xml elements. It serves as an important computation unit in xml pattern matching, such as twig joins. There exists many work on efficient structural joins. In particular, indexes can expedite structural joins by skipping unmatchable elements. A typical use of indexes is to retrieve, for a given element, all its ancestor (or descendant) elements from an indexed set. However we observed two possible limitations with such index probes, namely false hit and false locate. A false hit means that an index probe touches unnecessary data besides real results; a false locate stands for a (wasted) probe that has zero answers. Obviously false hit and false locate can affect negatively the efficiency of structural joins. In this paper, we challenge ourselves to develop new structural join algorithm with no false hit and no false locate. We illustrate that has the no false hit property (in contrast to ) and hence is a good candidate for our algorithm. For no false locate, we propose a new function called Location which tells the probing points that will result in matches. We design and implement the Location function using a space-efficient structure, and present our algorithm using together with the Location function. Extensive experiments show the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Given a query point q, finding the nearest neighbor (NN) object is one of the most important problem in computer science. In this paper, a bottom-up search algorithm for processing NN query in R-trees is presented. An additional data structure, hash, is introduced to increase the pruning capability of the proposed algorithm. Based on hash, whole data space is disjointly partitioned into n × n cells. Each cell contains the pointers of leaf nodes which intersect with the cell. The experiment shows that the proposed approach outperforms the existing NN search algorithms including the BFS algorithm which is known as I/O optimal algorithm. [ABSTRACT FROM AUTHOR]

To facilitate power saving via wireless data broadcast, index information is typically broadcast along with the data. By first accessing the broadcast index, the mobile client is able to predict the arrival time of the desired data. However, it suffers from the drawback that the client has to wait and tune for an index segment, in order to conserve battery power consumption. Moreover, the average time elapsed between the request for the data and its receipt may increase as a result of these additional messages. In this paper, we present a broadcast-based spatial query processing method designed to support K-NN(K-Nearest Neighbor) queries via wireless data broadcast. With the proposed schemes, the client can perform NN query processing without having to tune into an index segment. Experiments are conducted to evaluate the performance of the proposed methods. The resulting latency and energy consumption are close to the optimum values, as the analysis and simulation results indicate. [ABSTRACT FROM AUTHOR]

Continuous range monitoring on moving objects has been increasingly important in mobile environments. With the computational power and memory capacity on the mobile side, the distributed processing could relieve the server from high workload and provide real-time results. The existing distributed approaches typically partition the space into subspaces and associate the monitoring regions with those subspaces. However, the spatial irrelevance of the subspaces and the monitoring regions incurs the redundant processing as well as the extra communication cost. In this paper, we propose continuous expansion (CEM), a novel approach for efficient processing of continuous range monitoring in mobile environments. Considering the concurrent execution of multiple continuous range queries, CEM abstracts the dynamic relations between the movement of objects and the change of query answers, and introduces the concept of query view. The query answers are affected if and only if there are objects changing their current query views, which lead to the minimum transmission cost on the moving object side. CEM eliminates the redundant processing by handling the updates only from the objects that potentially change the answers. The experimental results show that CEM achieves the good performance in terms of server load and communication cost. [ABSTRACT FROM AUTHOR]

Incremental access can be essential for top-k queries, as users often want to sift through top answers until satisfied. In this paper, we propose the progressive rank (PR, for short) algorithm, a new non-blocking top-k query algorithm that deals with data items from remote sources via unpredictable, slow, or bursty network traffic. By accessing remote sources asynchronously and scheduling background processing reactively, PR hides intermittent delays in data arrival and produces the first few results quickly. Experiments results show that PR is an effective solution for producing fast query responses in the presence of slow and bursty remote sources, and can be scaled well. [ABSTRACT FROM AUTHOR]

In this paper, we propose a parallel multidimensional index structure and range search and k-NN search methods for the index structures. The proposed index structure is nP(processor)-n×mD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure increases fan-out and reduces the height of an index tree. Also, the proposed range search methods are designed to maximize I/O parallelism of the index structure. Finally, we present a new method to transform k-NN queries to range search queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures. [ABSTRACT FROM AUTHOR]

In this article, we consider energy-efficient implementation of the SQL join operation in sensor databases, when the join selection condition is a range predicate. Apart from two simple approaches, we propose distributed hash-join and index-join algorithms for implementation of range-join operations in sensor networks. Through extensive simulations, we show that hash-join as well as index-join approaches significantly outperform the simple approaches, even for moderately sized networks. Our experiments also reveal that although both approach scale well, the index-join algorithm performs better than the hash-join algorithm especially in large sensor networks. [ABSTRACT FROM AUTHOR]

RDF, the current standard for the representation of Web metadata, is based on a simple graph model that ensures logical interoperability between metadata formats. This model is however too limited to represent natively complex metadata structures such as sets, lists, structured property values and reified triples, which are simulated using blank nodes and built-in vocabularies. This situation tend to blur the semantics of RDF graphs and complicate the expression of queries. In this article, we extend RDF with n-ary statements and statement nesting, so that complex metadata structures are expressed natively. A query language over this extended RDF is also proposed. [ABSTRACT FROM AUTHOR]

To bridge the gap between the over increasing ubiquity of multimedia and the lack of approaches to deal with semantics of multimedia information, an important number of research efforts has been brought out lately. Nevertheless, a vast majority of proposed techniques deal with movies or other montage-edited media sources like TV-news, football matches, etc., which are characterized by efficient temporal granularity (frames, shots, scenes, etc.). Unfortunately, this latter property does not hold in environments like, e.g, multimedia meetings, making traditional approaches not quite suitable for such settings. In this paper we present our approach to conceptual modeling of multimedia data based on MADS, a spatio-temporal data model with multi-representation support. We also provide an example for the case of multimedia meetings. [ABSTRACT FROM AUTHOR]

Web service composition provides a way to build value-added services and web applications by integrating and composing existing web services. In this paper, a composite web service is modeled using queueing network for the purpose of performance analysis. Each component web service participating the composite web service corresponds to one service center. The control flow between component web services is represented by the Markov chain that describes the transition of customers between service centers. To perform performance analysis, the Markov chain should be known first. However, a web service is usually a black box and only its interfaces can be seen externally, so the internal control flow can be only estimated from history execution logs. This paper gives a method that mines the Markov chain of a composite web service from its execution logs. Then, bottlenecks identification and performance analysis are conducted for the queueing network model. Experimental results show that this model mining method is effective and efficient. [ABSTRACT FROM AUTHOR]

Compression aims to reduce the size of data without loss of information. Compaction is a special kind of compression in which the output is in the same language as the input. Compaction of an XML data forest produces a smaller XML forest, without losing any data. This paper develops a formal framework for the compaction of XML data and presents two compaction techniques. [ABSTRACT FROM AUTHOR]

An important database primitive for commonly used feature databases is the similarity join. It combines two datasets based on some similarity predicate into one set such that the new set contains pairs of objects of the two original sets. In many different application areas, e.g. sensor databases, location based services or face recognition systems, distances between objects have to be computed based on vague and uncertain data. In this paper, we propose to express the similarity between two uncertain objects by probability density functions which assign a probability value to each possible distance value. By integrating these probabilistic distance functions directly into the join algorithms the full information provided by these functions is exploited. The resulting probabilistic similarity join assigns to each object pair a probability value indicating the likelihood that the object pair belongs to the result set. As the computation of these probability values is very expensive, we introduce an efficient join processing strategy exemplarily for the distance-range join. In a detailed experimental evaluation, we demonstrate the benefits of our probabilistic similarity join. The experiments show that we can achieve high quality join results with rather low computational cost. [ABSTRACT FROM AUTHOR]

When hosting XML information on relational backends, a mapping has to be established between the schemas of the information source and the target storage repositories. A rich body of recent literature exists for mapping isolated components of XML Schema to their relational counterparts, especially with regard to table configurations. In this paper, we present the Elixir system for designing "industrial-strength" mappings for real-world applications. Specifically, it produces an information-preserving holistic mapping that transforms the complete XML world-view (XML schema with constraints, XML documents XQuery queries including triggers and views) into a full-scale relational mapping (table definitions, integrity constraints, indices, triggers and views) that is tuned to the application workload. A key design feature of Elixir is that it performs all its mapping-related optimizations in the XML source space, rather than in the relational target space. Further, unlike the XML mapping tools of commercial database systems, which rely heavily on user inputs, Elixir takes a principled cost-based approach to automatically find an efficient relational mapping. A prototype of Elixir is operational and we quantitatively demonstrate its functionality and efficacy on a variety of real-life XML schemas. [ABSTRACT FROM AUTHOR]

Directed Acyclic Graph(DAG) could be used for modeling subsumption hierarchies. Several labeling schemes have been proposed or tailored for indexing DAG in order to efficiently explore relationships in such hierarchy. However few of them can satisfy all the requirements in response time, space, and effect of updates simultaneously. In this paper, the prime number labeling scheme is extended for DAG. The scheme invests intrinsic mapping between integer divisibility and subsumption hierarchy, which simplifies the transitive closure computations and diminishes storage redundancy, as well as inherits the dynamic labeling ability from original scheme. Performance is further improved by introducing some optimization techniques. Our extensive experimental results show that prime number labeling scheme for DAG outperforms interval-based and prefix-based labeling schemes in most cases. [ABSTRACT FROM AUTHOR]

XQuery becomes a standard of the XML query language. Just like in SQL, XQuery allows nested expressions. To optimize XQuery processing, a lot of research has been done on normalization, i.e., transforming nested expressions to equivalent unnested ones. Previous normalization rules are classified into two categories - source-level and algebra-level - depending on whether a construct is specified by using a query language or an algebraic expression. In implementation point of view, we contend that the source-level rule is preferable to the algebra-level rule because algebras used for normalization are hard to be directly exploited in a typical DBMS. However, a complete set of source-level rules is yet to be developed. In this paper, we propose source-level rules for normalizing XQuery expressions and present an implementation mechanism. We show that our rules are correct and complete according to the nesting types classified by Kim. Our mechanism is easily implementable since it adapts the well-known Query Graph Model (QGM) representation. We have successfully implemented this mechanism into our XML DBMS named Odysseus/XML. [ABSTRACT FROM AUTHOR]

In semi-supervised classification, labels smoothness and cluster assumption are the key point of many successful methods. In graph-based semi-supervised classification, graph representations of the data are quite important. Different graph representations can affect the classification results greatly. Considering the two assumptions and graph representations, we propose a novel method to build a better graph for semi-supervised classification. The graph in our method is called m-step Markov random walk graph (mMRW graph). The smoothness of this graph can be controlled by a parameter m. We believe that a relatively much smoother graph will benefit transductive learning. We also discuss some benefits brought by our smooth graphs. A cluster cohesion based parameter learning method can be efficiently applied to find a proper m. Experiments on artificial and real world dataset indicate that our method has a superior classification accuracy over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As business and enterprises generate and exchange XML data more often, there is an increasing need for searching and querying XML data. A lot of researches have been done to match XML twig queries. However, as far as we know, very little work has examined the efficient processing of XML twig queries with not-predicates. In this paper, we propose a novel holistic twig join algorithm, called TwigStackList , which is designed for efficient matching an XML twig pattern with negation. We show that TwigStackList can identify a large query class to guarantee the I/O optimality. Finally, we run extensive experiments that validate our algorithm and show the efficiency and effectiveness of TwigStackList . [ABSTRACT FROM AUTHOR]

In this paper we present a quality model highlighting the completeness of sensor data with respect to its application. The model allows consistent handling of information loss as data propagates through a sensor network. The tradeoffs between various factors that influence completeness are quantified thereby allowing an integrated view of completeness at various levels in a system. The paper is presented in the context of the fast emerging field of smart spaces. All concepts in the paper have a foundation in real-life problems arising in this context. Preliminary implementation results are presented to illustrate the value of the completeness based approach versus one that does not use completeness. [ABSTRACT FROM AUTHOR]

It is difficult to index XML in practice, as there is a great deal of structure that can be included in an index. Using feedback from the database user's queries can assist the indexer by highlighting the exact structure in which the user is interested. In this paper, adaptive index structure for XML documents is presented which captures the structure given by branching path expressions, a very important class of queries. By leveraging existing infrastructure, the structure can handle updates both to the underlying data, and to itself, with little additional cost. [ABSTRACT FROM AUTHOR]

Keyword search is an effective approach for most users to search for information because they do not need to learn complex query languages or the underlying structures of the data. This paper focuses on effective keyword search in XML documents which are modeled as labeled trees. We first analyze the problems caused by the refinement of result granularity during XML keyword search and then propose to partition an XML document into XML fragments with the granularity of Minimal Information Unit (MIU). Furthermore, we present efficient index structures and the corresponding search algorithms. Finally, our comprehensive experiments demonstrate the benefits of our method over previously proposed methods in terms of result quality, index size and execution time. [ABSTRACT FROM AUTHOR]

In this paper, we explore the direct use of relations in information retrieval for precision-focused biomedical literature search. A relation is defined as a pair of two concepts which are semantically and syntactically related to each other. Unlike the traditional term-based IR models, our model represents a document by a set of controlled concepts and their binary relations. Since document level co-occurrence of two concepts, in many cases, does not mean this document really addresses their relationships, the direct use of relation may improve the precision of very specific search, e.g. searching documents that mention genes regulated by Smad4. For this purpose, we develop a generic ontology-based approach to extract concepts and their relations; a prototyped IR system supporting relation-based search is then built for Medline abstract search. We then use this novel IR system to improve the retrieval result of all official runs in TREC-2004 Genomics Track. The experiment shows promising performance of relation-based IR. The mean of P@100 (the precision of top 100 documents) for all 50 topics is raised from 26.37 %( the P@100 of the best run is 42.10%) to 53.69% while the recall is kept at an acceptable level of 44.31%. The experiment also demonstrates the expressiveness of relations for the representation of genomic information needs. [ABSTRACT FROM AUTHOR]

Graph has great expressive power to describe the complex relationships among data objects, and there are large graph datasets available. In this paper, we focus ourselves on processing a primitive graph query. We call it reachability query. The reachability query, denoted , is to find all elements of a type D that are reachable from some elements in another type A. The problem is challenging because the existing structural join algorithms, studied in XML query processing, cannot be directly applied to it, because those techniques make use of the tree-structure heavily. We propose a novel approach which can process reachability queries on the fly while keeping the space consumption small that is needed to keep the required information for processing reachability queries. In brief, our approach is based on 2-hop labeling for a directed graph G which consumes O(V·logE) space. We construct a novel join-index which is built on a small table and B+-tree. With the join-index, the high efficiency is achieved. We conducted extensive experimental studies, and we confirm that our approach can efficiently process reachability queries over a graph or a tree. [ABSTRACT FROM AUTHOR]

To facilitate the XML query processing, several kinds of labeling schemes have been proposed. Based on the labeling schemes, the ancestor-descendant and parent-child relationships in XML queries can be quickly determined without accessing the original XML file. Recently, more researches are focused on how to update the labels when nodes are inserted into the XML. However how to process the deleted labels are not discussed previously. We think that the deleted labels can be processed in two different directions: (1) reuse all the deleted labels to control the label size increasing speed and improve the query performance; (2) never reuse the deleted labels to query different versions of the XML data based on labeling schemes. In this paper, we firstly introduce our previous work, called QED, which can completely avoid the re-labeling in XML updates. Secondly based on QED we propose a new algorithm, called Reuse, which can reuse all the deleted labels to control the label size increasing speed; meanwhile the Reuse algorithm can completely avoid the re-labeling also. Thirdly to query different versions of the XML data, we propose another new algorithm, called NeverReuse, which is the only approach that never reuses any deleted labels. Extensive experimental results show that the algorithms proposed in this paper can control the label size increasing speed when reusing all the deleted labels, and is the only approach to query different versions of the XML data based on labeling schemes. [ABSTRACT FROM AUTHOR]

We study rules describing attribute dependencies in tables over domains with similarity relations. reads "for any two table rows: similar values of attributes from A imply similar values of attributes from B". The rules generalize ordinary functional dependencies in that they allow for processing of similarity of attribute values. Similarity is modeled by reflexive and symmetric fuzzy relations. We show a system of Armstrong-like derivation rules and prove its completeness (two versions). Furthermore, we describe a non-redundant basis of all rules which are true in a data table and present an algorithm to compute bases. [ABSTRACT FROM AUTHOR]

Conceptual Modelling plays a fundamental role in database design since Chen's Entity-Relationship (ER) model. In this paper we consider a conceptual model capable of capturing classes of objects with their attributes, relationships among classes, cardinality constraints in the participation of entities to relationships, and is-a relations among both classes and relationships. We provide a formal semantics for such model in terms of predicates and constraints over their extensions. We address the problem of containment of conjunctive queries over a conceptual schema, and we show an algorithm for solving the problem, that achieves better computational complexity than the techniques found in the literature. The results presented here are directly applicable in query answering on incomplete databases, and in data integration under constraints. [ABSTRACT FROM AUTHOR]

Workflow management is a fast evolving technology which can support business process reengineering and realize the full or partial automatic process of a business. A formal model of workflow should contain at least four major dimensions: the process, resource, case and time dimensions. Although much research has been devoted to this subject, to the best of our knowledge, no existing research is able to model workflow with the four dimensions at the same time. In this paper, a formal model, named as WF-RAPN (A Resource Assignment Petri Net for Workflow Management) is shown. The model extends traditional Petri Nets with resource assignments and case handling. To guarantee that resources are correctly consumed and execution time of each case is soundly calculated in the modeled processes, a formal set of WF-RAPN composition rules are designed and the proof of the correctness is also shown in the paper. Keywords: Petri nets, WF-RAPN, workflow, resource assignment. [ABSTRACT FROM AUTHOR]

We have constructed a graph database system where a query can be expressed intuitively as a diagram. The query result is also visualized as a diagram based on the intrinsic relationship among the returned data. In this database system, CORAL plays the role of a query execution engine to evaluate queries and deduce results. In order to understand the effectiveness of CORAL optimization techniques on visual query processing.We present and analyze the performance and scalability of CORAL's query rewriting strategies, which include Supplementary Magic Templates, Magic Templates, Context Factoring, Naïve Backtracking, and Without Rewriting method. Our research surprisingly shows that the Without Rewriting method takes the minimum total time to process the benchmark queries. Furthermore, CORAL's default optimization method Supplementary Magic Templates is not uniformly the best choice for every query. The "optimization" of visual queries is beneficial if one could select the right optimization approach for each query. [ABSTRACT FROM AUTHOR]

In numerous applications that deal with similarity search, a user may not have an exact specification of his information need and/or may not be able to formulate a query that exactly captures his notion of similarity. A promising approach to mitigate this problem is to enable the user to submit a rough approximation of the desired query and use relevance feedback on retrieved objects to refine the query. In this paper, we explore such a refinement strategy for a general class of structured similarity queries. Our approach casts the refinement problem as that of learning concepts using the tuples on which the user provides feedback as a labeled training set. Under this setup, similarity query refinement consists of two learning tasks: learning the structure of the query and learning the relative importance of query components. The paper develops machine learning approaches suitable for the two learning tasks. The primary contribution of the paper is the Refinement Activation Framework (RAF) that decides when each learner is invoked. Experimental analysis over many real life datasets shows that our strategy significantly outperforms existing approaches in terms of retrieval quality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel indexing method for similarity search in transaction databases where the frequency of database updates can be high. In our method, the incoming transactions are incrementally classified into clusters. The transactions in a cluster are represented using two features, namely the union and the intersection of all the transactions. Based on these two features, the transactions in a cluster are further divided into disjoint groups. As a result, all the transactions are organized as a two-level index structure. With this index, the insertion of a transaction can be quickly done because only a particular cluster needs to be modified. Moreover, when conducting a similarity search, we can compute for each level the lower and upper bounds on the distance between the query and each transaction in the cluster. Based on these bounds, the costs on the distance computation can be greatly reduced. [ABSTRACT FROM AUTHOR]

A number of similarity metrics have been used to measure the degree of web page changes in the literature. When a web page changes, the metrics often represent the change differently. In this paper, we first define criteria for web page changes to evaluate the effectiveness of the metrics in terms of six important types of web page changes. Second, we propose a new similarity metric appropriate for measuring the degree of web page changes. Using real web pages and synthesized pages, we analyze the five existing metrics (i.e., the byte-wise comparison, the TF∙IDF cosine distance, the word distance, the edit distance, and the shingling) and ours under the proposed criteria. The analysis result shows that our metric represents the changes more effectively than other metrics. We expect that our study can help users select an appropriate metric for particular web applications. [ABSTRACT FROM AUTHOR]

Clustering web users is one of the most important research topics in web usage mining. Existing approaches cluster web users based on the snapshots of web user sessions. They do not take into account the dynamic nature of web usage data. In this paper, we focus on discovering novel knowledge by clustering web users based on the evolutions of their historical web sessions. We present an algorithm called COWES to cluster web users in three steps. First, given a set of web users, we mine the history of their web sessions to extract interesting patterns that capture the characteristics of their usage data evolution. Then, the similarity between web users is computed based on their common interesting patterns. Then, the desired clusters are generated by a partitioning clustering technique. Web user clusters generated based on their historical web sessions are useful in intelligent web advertisement and web caching. [ABSTRACT FROM AUTHOR]

Web services oriented computing is a hot topic for many e-commerce applications recently. It dramatically speeds up the application process and becomes more agile in responding to changing business needs. In this paper, we study the concurrency control problems among interleaved transactions for web services oriented e-commerce. We propose a new method called two-phase locking with adjustable slack time (2PL-AST) based on a high priority mechanism, which resolves the concurrent data access for both real-time and non-real-time support operations. The design issue aims to meet the deadline requirements of real-time transactions and minimize the response time of non-real-time transactions in the web services family at the same time. Experiments are conducted to evaluate the performance under different concurrency control protocols. [ABSTRACT FROM AUTHOR]

EXtensible Markup Language (XML) has rapidly gained importance as a mechanism for the exchange of information amongst heterogeneous sources over the web. In order to deal with the challenging task of managing the large volumes of data appearing, encoded in XML transactional databases, the need to explore the XML Document Warehouse (XDW) approach is initiated. The applications of the Requirement Engineering (RE) process and Object-Oriented conceptual models have proven their usefulness in building successful software models and solutions. In this paper we introduce an integration methodology for the development of XDWs. Initially we provide a formal notation of the structural design for the XDW conceptual model. Secondly we focus on deriving requirements for the XDWs by exploring the Goal-Question Metric (GQM) approach. We adapt and extend this concept to XDWs and introduce a method for developing warehouse requirements considering user viewpoints and organizational objectives. The implementation of our proposed warehouse requirement derivation model is demonstrated using a case study example extracted from a simplified real-world scenario. [ABSTRACT FROM AUTHOR]

In order to efficiently evaluate range-aggregate queries in data warehouse environments, several works on data cubes (such as the aggregate cubetree) are proposed. In the aggregate cubetree, each entry in every node stores the aggregate values of its corresponding subtree. Therefore, range-aggregate queries can be processed without visiting the child nodes whose parent nodes are fully included in the query range. However, the aggregate cubetree does not take range queries using partial dimensions and range queries without aggregation operations into account. That is, 1) a great deal of information that is irrelevant to the queries also has to be read from the disk for partially-dimensional range queries and 2) while it improves the performance of range queries with aggregate operations, it degrades the performance of the range queries without aggregate operations. In this paper, we proposed a novel index structure, called Aggregate-Tree (denoted as Ag-Tree), which gets rid of the above-mentioned weaknesses of the aggregate cubetree without any side effects. The experiments and discussions presented in this paper indicate that the new proposal is significant for range queries in data warehouse environments. [ABSTRACT FROM AUTHOR]