Effective evaluation of expressive music performance should not only assess technical and interpretive quality but also support efficient practice by identifying musically critical segments. However, traditional expert feedback is often limited by accessibility and scalability. To address this, we investigate how musically critical segments can be automatically identified and interpreted to support personalized assessment of piano performance. We propose a framework that identifies expressive deviations in piano performance by comparing extracted feature values with reference guidelines derived from music sheet analysis and expert annotations. Critical segments are detected via SHAP-based feature importance and change-point analysis, while interpretable annotation is generated using a large language model, conditioned on feature descriptions and quantified deviations. We evaluate our framework on the PercePiano dataset and newly collected annotations, showing consistent improvements in predicted overall scores of the performance after applying the generated annotation. Annotations generated using a finetuned feature extraction model improved predicted scores by up to 8.76%, with greater alignment with expert labels in both segment coverage and overlap. SHAP-based analysis confirms that the model identifies musically important features, enhancing both interpretability of annotation and its relevance to musical evaluation. Our results demonstrate that the proposed framework produces interpretable, musically meaningful annotations aligned with expert evaluations, and can serve as a foundation for scalable, AI-assisted music education and assessment. Our code is available at: https://github.com/Hyerim-Jeon/critical_segments.

Graph Neural Networks have achieved notable success, yet explaining their rationales remains a challenging problem. Existing methods, including post-hoc and interpretable approaches, have numerous limitations. Post-hoc methods treat models as black boxes and can mislead users, while interpretable models often overlook user-centric explanations. Furthermore, most existing methods do not carefully consider the user’s perception of explanations, potentially resulting in explanation-user mismatches. To address these problems, we propose a novel interpretable concept-matching model to enhance GNN interpretability and prediction accuracy. The proposed model extracts frequent concepts from input graphs using the graph information bottleneck theory and modified constraints. These concepts are managed in an in-memory concept corpus for efficient inference lookups and explanation generation. Various explanation construction features are implemented based on the concept corpus and the discovery module, aiming to fulfill diverse user preferences. Extensive experiments and a user study validate the performance of the proposed approach, showcasing its potential for improving model accuracy and interpretability.

users in a social network to maximize the expected number of users influenced by the selected users (called influence spread), has been extensively studied, existing works neglected the fact that the location information can play an important role in influence maximization. Many real-world applications such as location-aware word-of-mouth marketing have location-aware requirement. In this paper we study the location-aware influence maximization problem. One big challenge in location-aware influence maximization is to develop an efficient scheme that offers wide influence spread. To address this challenge, we propose two greedy algorithms with 1-1/e approximation ratio. To meet the instant-speed requirement, we propose two efficient algorithms with ε· (1-1/e) approximation ratio for any ε ∈ (0,1]. Experimental results on real datasets show our method achieves high performance while keeping large influence spread and significantly outperforms state-of-the-art algorithms.

String similarity join is an important operation in data integration and cleansing that finds similar string pairs from two collections of strings. More than ten algorithms have been proposed to address this problem in the recent two decades. However, existing algorithms have not been thoroughly compared under the same experimental framework. For example, some algorithms are tested only on specific datasets. This makes it rather difficult for practitioners to decide which algorithms should be used for various scenarios. To address this problem, in this paper we provide a comprehensive survey on a wide spectrum of existing string similarity join algorithms, classify them into different categories based on their main techniques, and compare them through extensive experiments on a variety of real-world datasets with different characteristics. We also report comprehensive findings obtained from the experiments and provide new insights about the strengths and weaknesses of existing similarity join algorithms which can guide practitioners to select appropriate algorithms for various scenarios.

Existing route recommendation systems have two main weaknesses. First, they usually recommend the same route for all users and cannot help control traffic jam. Second, they do not take full advantage of real-time traffic to recommend the best routes. To address these two problems, we develop a real-time route recommendation system, called R3, aiming to provide users with the real-time-traffic-aware routes. R3 recommends diverse routes for different users to alleviate the traffic pressure. R3 utilizes historical taxi driving data and real-time traffic data and integrates them together to provide users with real-time route recommendation.

String similarity search is a fundamental operation in many areas, such as data cleaning, information retrieval, and bioinformatics. In this paper we study the problem of top-k string similarity search with edit-distance constraints, which, given a collection of strings and a query string, returns the top-k strings with the smallest edit distances to the query string. Existing methods usually try different edit-distance thresholds and select an appropriate threshold to find top-k answers. However it is rather expensive to select an appropriate threshold. To address this problem, we propose a progressive framework by improving the traditional dynamic-programming algorithm to compute edit distance. We prune unnecessary entries in the dynamic-programming matrix and only compute those pivotal entries. We extend our techniques to support top-k similarity search. We develop a range-based method by grouping the pivotal entries to avoid duplicated computations. Experimental results show that our method achieves high performance, and significantly outperforms state-of-the-art approaches on real-world datasets.

XML is a more desirable format for modeling and storing clinical data in EMR (Electronic medical record) applications for its extendibility; however, existing EMR systems either are built on top of RDBMS or file systems or lack of support for complex and large scale healthcare applications, such as treatment effectiveness analysis and procedure optimization. SAP Technology Lab, China is developing a clouds-enabled information appliance, Xbase, built on top of Hadoop, which is the first XML-based information appliance designed specifically for large scale and complex healthcare applications. XML presents a different set of challenges for query processing, indexing, parallelism, and distributed computing using existing Hadoop's APIs as well as its HDFS storage infrastructure and MapReduce framework. In this paper, we describe system architecture and internal designs of Xbase as well as how the indexing is mapped to RDBMS and Hadoop. We also discuss why we select Hadoop over other candidates, such as Hbase, Google's Bigtable, and Hive.

Many large companies, especially those in financial and insurance service sectors, approach the market with a decentralized management structure, such as by line of business or geographical market segments. However, these companies require access to distributed and possibly heterogeneous data sources for corporate level decision making. In this paper, we focus on challenges of supporting a decision support system (DSS) based on a hybrid approach (i.e. a federation system with replication of frequently accessed remote data sources) for time-sensitive agile business intelligence applications. The response time requirement (and a realistic goal) for such a DSS is near real time (i.e. 2 ~ 3 minutes to 20 ~ 30 minutes). The users of a DSS care about not only the response time but also the time stamp of the business operation reports since out-dated reports introduce uncertainty and risks to decision-making. Thus, the information value of a report decreases as time passes. We present a framework of correlation aware synchronization of replicas used in DSS to optimize information values of business reports as a whole. The framework exploits correlation of usage and synchronization latency of replicas in a single query and a workload of queries for an optimal synchronization schedule. We have conducted extensive evaluations based on both TPC-H and synthetic workload. The proposed correlation aware synchronization effectively improves up to 50% of information value comparing with fixed synchronization plans on average.

The high cost of creating and maintaining software and hardware infrastructures for delivering services to businesses has led to a notable trend toward the use of third-party service providers, which rent out network presence, computation power, and data storage space to clients with infrastructural needs. These third party service providers can act as data stores as well as entire software suites for improved availability and system scalability, reducing small and medium businesses' burden of managing complex infrastructures. This is called information/application outsourcing or software as a service (SaaS). Emergence of enabling technologies, such as service oriented architectures (SOA), virtual machines, and cloud computing, contribute to this trend. Scientific Grid computing, on-line software services, and business service networks are typical examples leveraging database and software as service paradigm. In this paper, we survey the technologies used to enable SaaS paradigm as well as the current offerings on the market. We also outline research directions in the field.

Enterprises may have multiple database systems spread across the organization for redundancy or for serving different applications. In such systems, query workloads can be distributed across different servers for better performance. A materialized view, or Materialized Query Table (MQT), is an auxiliary table with pre-computed data that can be used to significantly improve the performance of a database query. In this paper, we propose a framework for coordinating execution of OLAP query workloads across a database cluster with shared nothing architecture. Such coordination is complex since we need to consider (1) the time to build the MQTs, (2) the query execution impact of the MQTs, (3) whether the MQTs can fit in the disk space limitation, (4) server computation power, and (5) the effectiveness of the scheduling and placement algorithms in deriving a combination of configurations so that the workload can be completed in the shortest time period. We frame the problem as a combinatorial problem with a solution space that is exponential in the number of queries, MQTs, and servers. We provide a stochastic search heuristic that finds a near-optimal mapping of queries-to-servers and MQTs-to-servers within an arbitrarily bounded time and compare our solution with an exhaustive search and three standard greedy algorithms. Our search implementation produced schedules within 9% of the optimal found through an exhaustive search and produced better solutions than typical greedy algorithms for both TPC-H and synthetic benchmarks under a variety of experiments. For a key trial where disk space is limited, it produced 15% better results than the next best competitor, corresponding to an absolute wall clock advantage of over 10 hours.

A materialized view, or Materialized Query Table (MQT), is an auxiliary table with precomputed data that can be used to significantly improve the performance of a database query. Previous research efforts have focused on finding the best candidate MQT set, with a common static heuristic being to greedily pre-materialize the MQTs prior to executing the workload. While this approach is sound when the size of the MQT set on disk is small, it will not be able to pre-materialize all MQTs and indexes when faced with real-world disk limits and view maintenance costs, and thus a static heuristic will fail to exploit the potentially large benefits of those MQTs not selected for materialization. In this paper we present an automated, dynamic MQT management scheme that materializes views and creates indexes in an on-demand fashion as a workload executes and manages them with an LRU cache. In order to maximize the benefit of executing queries with MQTs, the scheme makes an adaptive tradeoff between the MQT materializations, the base table accesses, and the benefit of MQT hits in the cache. To find the workload permutation that produces the overall highest net benefit, we use a genetic algorithm to search the N! solution space, and to avoid materializing seldom-used MQTs, we prune the set of MQT candidates. We ran our dynamic management on a TPC-H workload and found that our scheme produces higher benefit across a variety of scenarios; we demonstrate over 60% improvement in MQT benefit under harsh conditions when the size of available MQTs with indexes is larger than the cache.

Enterprises may use a service-oriented architecture (SOA) to provide a streamlined interface to their business processes. To scale up the system, each tier in a composite service usually deploys multiple servers for load distribution and fault tolerance. Such load distribution across multiple servers within the same tier can be viewed as horizontal load distribution. One limitation of this approach is that load cannot be further distributed when all servers in the same tier are fully loaded. In complex multi-tiered systems, a single business process may actually be implemented by multiple different computation pathways among the tiers, each with different components, in order to provide resiliency and scalability. Such SOA-based enterprise computing with multiple implementation options gives opportunities for vertical load distribution across tiers. In this paper, we propose a requestrouting framework for SOA-based enterprise computing that takes into consideration both horizontal and vertical load distribution. Through experimentation we show that our algorithm and methodology scale well up to a large system configuration comprising up to 1000 workflow requests to a complex composite service with multiple implementations. We also show that a combination of both horizontal and vertical load distributions gives the maximum flexibility to improve performance and fault tolerance.

Many large enterprises require access to distributed data warehouses for business intelligence (BI) applications. Typically distributed data warehouses are integrated into a centralized data warehouse for the benefit of easy maintenance. However, this approach needs to overcome the complexity of data loading and job scheduling as well as scalability issues. On the other hand, the approach of a fully federated system may not be feasible for data intensive BI applications. The hybrid approach via intelligent data placement is more flexible and applicable than the centralized or full-federation configuration. The current implementation of the hybrid approach to integrating distributed data warehouses is to aggregate selected data from various remote sources as materialized views and cache them at the federation server to improve the performance of complex BI query workloads. In this paper, we propose an improvement that recommends materialized query tables (MQTs) for backend servers for the benefits of load distribution and easy maintenance of aggregated data in conjunction with the current hybrid approach of data placement. Our approach considers the correlation between backend servers and recommends MQTs that are well coordinated among the backend servers and optimized for a given workload. We also exploit the parallelism property among the backend servers to make our approach run almost linearly (in contrast to exponentially) with respect to the number of backend servers, without sacrificing its recommendation quality. Experimental evaluations validate the effectiveness and efficiency of our approach.

A materialized view or Materialized Query Table (MQT) is an auxiliary table with precomputed data that can be used to significantly improve the performance of a database query. AMaterialized Query Table Advisor (MQTA) is often used to recommend and create MQTs. The state-of-the-art MQTA works in a standalone database server where MQTs are placed on the same server as that in which the base tables are located. The MQTA does not apply to a federated or scaleout scenario in which MQTs need to be placed on other servers close to applications (i.e. a frontend database server) for offloading the workload on the backend database server. In this paper, we propose a Data Placement Advisor (DPA) and load balancing strategies for multi-tiered database systems. Built on top of the MQTA, DPA recommends MQTs and advises placement strategies for minimizing the response time for a query workload. To demonstrate the benefit of the data placement advising, we implemented a prototype of DPA that works with theMQTA in the IBM® DB2® Universal Database^TM (DB2 UDB) and the IBM WebSphere® Information Integrator (WebSphere II). The evaluation results showed substantial improvements of workload response times when MQTs are intelligently recommended and placed at a frontend database server subject to space and load characteristics for TPC-H and OLAP type workloads.

Current federated systems deploy cost-based query optimization mechanisms; i.e., the optimizer selects a global query plan with the lowest cost to execute. Thus, cost functions influence what remote sources (i.e. equivalent data sources) to access and how federated queries are processed. In most federated systems, the underlying cost model is based on database statistics and query statements; however, the system load of remote sources and the dynamic nature of the network latency in wide area networks are not considered. As a result, federated query processing solutions can not adapt to runtime environment changes, such as network congestion or heavy workloads at remote sources. We present a novel system architecture that deploys a query cost calibrator to calibrate the cost function based on system load and network latency at the remote sources and consequently indirectly "influences" query routing and load distribution in federated information systems.

For most Web-based applications, contents are created dynamically based on the current state of a business, such as product prices and inventory, stored in database systems. These applications demand personalized content and track user behavior while maintaining application integrity. Many of such practices are not compatible with Web acceleration solutions. Consequently, although many web acceleration solutions have shown promising performance improvement and scalability, architecting and engineering distributed enterprise Web applications to utilize available content delivery networks remains a challenge. In this paper, we examine the challenge to accelerate J2EE-based enterprise web applications. We list obstacles and recommend some practices to transform typical database-driven J2EE applications to cache friendly Web applications where Web acceleration solutions can be applied. Furthermore, such transformation should be done without modification to the underlying application business logic and without sacrificing functions that are essential to e-commerce. We take the J2EE reference software, the Java PetStore, as a case study. By using the proposed guideline, we are able to cache more than 90% of the content in the PetStore and scale up the Web site more than 20 times.

With the wide availability of content delivery networks, many e-commerce Web applications utilize edge cache servers to cache and deliver dynamic contents at locations much closer to users, avoiding network latency. By caching a large number of dynamic content pages in the edge cache servers, response time can be reduced, benefiting from higher cache hit rates. However this is achieved at the expense of higher invalidation cost. On the other hand, a higher invalidation cost leads to a longer invalidation cycle (time to perform the invalidation check on the pages in caches) at the expense of freshness of cached dynamic content. In this paper we propose a freshness-driven adaptive dynamic content caching technique, which monitors response time and invalidation cycle length and dynamically adjusts caching policies. We have implemented the proposed technique within NECs CachePortal Web acceleration solution. The experimental results show that the proposed technique consistently maintains the best content freshness to users. The experimental results also show that even a Web site with dynamic content caching enabled can further benefit from deployment of our solution with improvement of its content freshness up to 10 times especially during heavy traffic.

Wide-area database replication technologies and the availability of content delivery networks allow Web applications to be hosted and served from powerful data centers. This form of application support requires a complete Web application suite to be distributed along with the database replicas. A major advantage of this approach is that dynamic content is served from locations closer to users, leading into reduced network latency and fast response times. However, this is achieved at the expense of overheads due to (a) invalidation of cached dynamic content in the edge caches and (b) synchronization of database replicas in the data center. These have adverse effects on the freshness of delivered content. In this paper, we propose a freshness-driven adaptive dynamic content caching, which monitors the system status and adjusts caching policies to provide content freshness guarantees. The proposed technique has been intensively evaluated to validate its effectiveness. The experimental results show that the freshness-driven adaptive dynamic content caching technique consistently provides good content freshness. Furthermore, even a Web site that enables dynamic content caching can further benefit from our solution, which improves content freshness up to 7 times, especially under heavy user request traffic and long network latency conditions. Our approach also provides better scalability and significantly reduced response times up to 70% in the experiments.

Since the Web encourages hypertext and hypermedia document authoring (e.g., HTML or XML), Web authors tend to create documents that are composed of multiple pages connected with hyperlinks. A Web document may be authored in multiple ways, such as: (1) all information in one physical page, or (2) a main page and the related information in separate linked pages. Existing Web search engines, however, return only physical pages containing keywords. We introduce the concept of information unit, which can be viewed as a logical Web document consisting of multiple physical pages as one atomic retrieval unit. We present an algorithm to efficiently retrieve information units. Our algorithm can perform progressive query processing. These functionalities are essential for information retrieval on the Web and large XML databases. We also present experimental results on synthetic graphs and real Web data.

Web performance is a key differentiation among content providers. Snafus and slowdowns at major web sites demonstrate the difficulty that companies face trying to scale to a large amount of web traffic. One solution to this problem is to store web content at server-side and edge-caches for fast delivery to the end users. However, for many e-commerce sites, web pages are created dynamically based on the current state of business processes, represented in application servers and databases. Since application servers, databases, web servers, and caches are independent components, there is no efficient mechanism to make changes in the database content reflected to the cached web pages. As a result, most application servers have to mark dynamically generated web pages as non-cacheable. In this paper, we describe the architectural framework of the CachePortal system for enabling dynamic content caching for database-driven e-commerce sites. We describe techniques for intelligently invalidating dynamically generated web pages in the caches, thereby enabling caching of web pages generated based on database contents. We use some of the most popular components in the industry to illustrate the deployment and applicability of the proposed architecture.

Site maps are essential to assist users in navigating a Web site. Most of the site maps are constructed manually and are static. However, di erent users may have di erent preferences and purposes for using a Web site. For example, a user may want to see a more detailed map while another user prefers a more abstract map. Two users looking for di erent topics at a large portal site would bene t more from two site maps with di erent focuses than a single map. In this paper, we present a technique for automatically constructing multi-granular and topic-focused site maps by utilizing directory paths, page contents, and link structures. In these site maps, the Web site topology is preserved and document importance, indicated by citation and semantic relevancy to user's topics of interest, is used for prioritizing the presentation of pages and directories. Experiments on real Web data have been conducted to validate the usefulness of the technique.

Article Retrieving and organizing web pages by "information unit" Share on Authors: Wen-Syan Li C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile , K. Selçuk Candan C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile , Quoc Vu C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile , Divyakant Agrawal C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 230–244https://doi.org/10.1145/371920.372057Online:01 April 2001Publication History 66citation1,478DownloadsMetricsTotal Citations66Total Downloads1,478Last 12 Months11Last 6 weeks3 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

article Free Access Share on PowerBookmarks: a system for personalizable Web information organization, sharing, and management Authors: Wen-Syan Li C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Quoc Vu C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Edward Chang C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Divyakant Agrawal View Profile , Kyoji Hirata View Profile , Sougata Mukherjea View Profile , Yi-Leh Wu View Profile , Corey Bufi View Profile , Chen-Chuan Kevin Chang View Profile , Yoshinori Hara C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CA C&C Research Laboratories, NEC USA, Inc., 110 Rio Robles, M/S SJ1OO, San Jose, CAView Profile , Reiko Ito 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile , Yutaka Kimura 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile , Kezuyuki Shimazu 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile , Yukiyoshi Saito 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, Japan 5th Development Department, 2nd Computers Software Division, NEC Corporation, 1-10, Nisshin-Cho, Fuchu-Shi, Tokyo 183-8501, JapanView Profile Authors Info & Claims ACM SIGMOD RecordVolume 28Issue 2June 1999 pp 565–567https://doi.org/10.1145/304181.304578Online:01 June 1999Publication History 22citation480DownloadsMetricsTotal Citations22Total Downloads480Last 12 Months6Last 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Image data is structurally more complex than traditional types of data. An image can be viewed as a compound object containing many sub-objects. Each sub-object corresponds to image regions that are visually and semantically meaningful (e.g. car, man, etc.). We introduce a hierarchical structure for image modeling that supports image retrieval, at both the whole-image and object levels, using combinations of semantic expressions and visual examples. We introduce an image database system called SEMCOG (SEMantics and COGnition-based image retrieval). SEMCOG aims at integrating semantics- and cognition-based approaches and allows queries based on object-level information. We present a formal definition of a multimedia query language, we give details of the database's implementation and query processing, and we discuss our methods for merging similarities from different types of query criteria.

Semantic integration and data integration are two main processes that multidatabase systems need to employ in order to support interoperability. Both these processes involve uncertainty when attribute correspondences and global IDs are unknown or imprecise. The role-set approach is a new conceptual framework for data integration in multidatabase systems that maintains the materialization autonomy of local database systems by presenting the answer to a query as a set of sets representing the distinct intersections between the relations corresponding to the various roles played by an entity. In this article, we present an approach for dynamic database integration and query processing in the absence of information about attribute correspondences and global IDs. We define different types of equivalence conditions for the construction of global IDs. We propose a strategy based on ranked role-sets that makes use of an automated semantic integration procedure based on neural networks to determine candidate global IDs. The data integration and query processing steps then produce a number of role-sets, ranked by the similarity of the candidate IDs. © 1998 John Wiley & Sons, Inc.

2 SEMCOG Approach We argue that image retrieval based on either approach alone is not sufficient in terms of modeling and query specification flexibility y. We also argue that a visual query interface which is capability of visualizing target images is essential. SEMCOG[l] (SEMantics and COGnitionbased image retrieval) aims at integrating semautics and cognition-based approaches to give users a greater flexibility to pose queries. SEMCOG’S image matching is based on objects in the images rather than the whole images. In SEMCOG, a query “Retrieve all images in which there is a man to the right of a car and the man looks like this image” can be posed using combinations of semantics and visual expressions. The queries are posed in the way of specifying image objects and their layouts using a visual query interface, IFQ (In Frame Query), rather than complicated multimedia database query languages. The user’s query can be simplified as a mental model shown at the top of Figure 1. She then specifies her mental model using IFQ using combinations of visual examples or semantics. IFQ window shows a query “Retrieve

Informationsystems, which are based on database management systems, consist of two major parts: A alztabase schema and applications.We extend the view of sofmare reuse to encompass not only application programs, but also the database schema.In ~his pape~we develop a technique for clustering and retrieving schema components based on their semantics.Our technique is based on both faceted classl@cation and neural network pattern recognition.The advantages of our approach include the user only needs to index a small portion of the components using faceted classification yet all other components are also available for retrieval by automatedclustering.An application using this technique in building a data warehouse is discussed.

No abstract available.