3479382
Semantic Data Management: Towards Querying Data with their Meaning
Relational database management systems are constantly being extended and augmented to accommodate data in different domains. Recently, with the increasing use of ontology in various applications, the need to support ontology, especially the related inferencing operation, in DBMS has become more concrete and urgent. However, manipulating knowledge along with relational data in DBMSs is not a trivial undertaking due to the mismatch in data models. In this paper, we introduce a framework for managing relational data and hierarchical domain knowledge together. Our framework persists taxonomies contained in ontologies by leveraging XML support in hybrid relational-XML DBMSs (e.g., IBM's DB2 v9) and rewrites ontology-based semantic matching queries using the industry-standard query languages, SQL/XML and XQuery. Compared with previous approaches, our approach does not materialize transitive closures of ontological relationships to support inferencing. Consequently, our method has wide applicability and good performance.
5103963
Adaptive Load Diffusion for Multiway Windowed Stream Joins
In this paper, we present an adaptive load diffusion operator to enable scalable processing of multiway windowed stream joins (MWSJs) using a cluster system. The load diffusion is achieved by a set of novel semantics-pre serving tuple routing algorithms. Different from previous work, the load diffusion operator can (1) preserve the MWSJ semantics while spreading tuples to different hosts for parallel join processing; (2) achieve fine-grained load balancing among distributed hosts; and (3) perform semantics-preserving online adaptations to maintain optimal performance in dynamic stream environments. We have implemented a prototype of the distributed MWSJ framework on top of the System S distributed stream processing system. Our experiment results based on both real data streams and synthetic workloads show that the load diffusion algorithms can efficiently scale-up the performance of MWSJ processing with low overhead.
14876394
User Defined Aggregates for Logical Data Languages
A new wave of data-intensive and knowledge-based applications|such as data mining and decision support|require the introduction of complex application-speciic aggregate functions. In this paper, we propose extensions for deductive database systems to support these new applications. We develop constructs, formal semantics, and implementation techniques for user-deened aggregates, and describe their realization in an extended LDL++ system recently built at UCLA. With these extensions, the system can support on-line aggregation, roll-ups for data cubing, temporal aggregates for time-series, iceberg queries, and other recently proposed operators used in decision support and data mining procedures. We then discuss the application of this technology to other DBMSs, and in particular to the SQL3 speciications that support the notion of user-deened aggregates. We show that SQL3 suuers from limitations that severely restrict its use in new applications; thus we propose simple extensions similar to those used for LDL++ to overcome such limitations.
5368193
Progressive modeling
Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and training time. If the accuracy of the produced model is too low, all the computing resources are misspent. In this paper we propose a progressive modeling framework. In progressive modeling, the learning algorithm estimates online both the accuracy of the final model and remaining training time. If the estimated accuracy is far below expectation, the user can terminate training prior to completion without wasting further resources. If the user chooses to complete the learning process, progressive modeling will compute a model with expected accuracy in expected time. We describe one implementation of progressive modeling using ensemble of classifiers.
1498767
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.
18937658
Mining associations by pattern structure in large relational tables
Association rule mining aims at discovering patterns whose support is beyond a given threshold. Mining patterns composed of items described by an arbitrary subset of attributes in a large relational table represents a new challenge and has various practical applications, including the event management systems that motivated this work. The attribute combinations that define the items in a pattern provide the structural information of the pattern. Current association algorithms do not make full use of the structural information of the patterns: the information is either lost after it is encoded with attribute values, or is constrained by a given hierarchy or taxonomy. Pattern structures convey important knowledge about the patterns. We present an architecture that organizes the mining space based on pattern structures. By exploiting the interrelationships among pattern structures, execution times for mining can be reduced significantly. This advantage is demonstrated by our experiments using both synthetic and real-life datasets.
35913610
Knowledge Discovery Through Self-Organizing Maps: Data Visualization and Query Processing
Abstract. 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.
2984995
Query-By-Example Technique for Retrieving Cover Versions of Popular Songs with Similar Melodies
Retrieving audio material based on audio queries is an important and challenging issue in the research field of content-based access to popular music. As part of this research field, we present a preliminary investigation into retrieving cover versions of songs specified by users. The technique enables users to listen to songs with an identical tune, but performed by different singers, in different languages, genres, and so on. The proposed system is built on a query-by-example framework, which takes a fragment of the song submitted by the user as input, and returns songs similar to the query in terms of the main melody as output. To handle the likely discrepancies, e.g., tempos, transpositions, and accompaniments between cover versions and the original song, methods are presented to remove the non-vocal portions of the song, extract the sung notes from the accompanied vocals, and compare the similarities between the sung note sequences.
15210668
A Music Retrieval System Based on Query-by-Singing for Karaoke Jukebox
This paper investigates the problem of retrieving Karaoke music by singing. The Karaoke music encompasses two audio channels in each track: one is a mix of vocal and background accompaniment, and the other is composed of accompaniment only. The accompaniments in the two channels often resemble each other, but are not identical. This characteristic is exploited to infer the vocal’s background music from the accompaniment-only channel, so that the main melody underlying the vocal signals can be extracted more effectively. To enable an efficient and accurate search for a large music database, we propose a phrase onset detection method based on Bayesian Information Criterion (BIC) for predicting the most likely beginning of a sung query, and adopt a multiple-level multiple-pass Dynamic Time Warping (DTW) for melody similarity comparison. The experiments conducted on a Karaoke database consisting of 1,071 popular songs show the promising results of query-by-singing retrieval for Karaoke music.
9669585
Conceptual construction on incomplete survey data
The raw survey data for data mining are often incomplete. The issues of missing data in knowledge discovery are often ignored in data mining. This article presents the conceptual foundations of data mining with incomplete survey data, and proposes query processing for knowledge discovery and a set of query functions for the conceptual construction in survey data mining. Through a case, this paper demonstrates that conceptual construction on incomplete data can be accomplished by using artificial intelligence tools such as self-organizing maps.
7829422
Fast Relative Depth Computation for an Active Stereo Vision System
Real-time computation of exact depth is not feasible in an active vision setup. Instead, reliable relative depth information which can be rapidly computed is preferred. In this paper, a stereo cue for computing relative depth obtained from an active stereo vision system is proposed. The proposed stereo cue can be computed purely from the coordinates of points in the stereo pair. The computational cost required is very low. No camera calibration or prior knowledge of the parameters of the stereo vision system is required. We show that the relationship between the relative depth cue and the actual depth in the three-dimensional (3D) space is monotonic. Such a relation is maintained even when the focal length and the vergence angle are changed, so long as the focal lengths of the two cameras are similar. Therefore, real-time implementation in an active vision setup can be realized. Stability analysis shows that the proposed method will be stable in practical situations, unless the stereo camera diverges. Experimental results are presented to highlight the properties and advantages of the proposed method.