7789558
Indexing weighted-sequences in large databases
We present an index structure for managing weighted-sequences in large databases. A weighted-sequence is defined as a two-dimensional structure where each element in the sequence is associated with a weight. A series of network events, for instance, is a weighted-sequence in that each event has a timestamp. Querying a large sequence database by events' occurrence patterns is a first step towards understanding the temporal causal relationships among the events. The index structure proposed enables us to efficiently retrieve from the database all subsequences, possibly noncontiguous, that match a given query sequence both by events and by weights. The index method also takes into consideration the nonuniformfrequency distribution of events in the sequence data. In addition, our method finds a broad range of applications in indexing scientific data consisting of multiple numerical columns for discovery of correlations among these columns. For instance, indexing a DNA microarray that records expression levels of genes under different conditions enables us to search for genes whose responses to various experimental perturbations follow a given pattern. We demonstrate, using real-world data sets, that our method is effective and efficient.
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.
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.
2129781
ATLaS: A Native Extension of SQL for Data Mining
A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new application domains. Considerable efforts by database researchers and commercial DBMS vendors have led to major extensions; yet there remain important applications—particularly data mining—that are not supported well in SQL-3. Thus, there is a pressing need for more general mechanisms for extending SQL and dealing with new application areas, particularly database-centric data mining. To satisfy this need, we allow database users to add new table functions and stream-oriented aggregate functions by defining them in SQL—rather than in external procedural languages as O-R DBMSs currently do. This simple extension turns SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP aggregates, time-series queries, stream-oriented processing, and data mining functions. In addition to adding great power and flexibility to SQL, these extensions are conducive to performance and data independence. The paper also discusses the system we have developed to support these SQL extensions, and the architecture and techniques used in its realization.
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.
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.
16600904
A services oriented framework for next generation data analysis centers
Over the past decade, advances in computational and sensor technology have enabled us to dynamically collect vast amounts of data from observations, health screening tests, simulations, and experiments at an ever-increasing pace. Knowledge discovery and data mining is an iterative process concerned with deriving interesting, non-obvious, and useful patterns and models from such large volumes of data. Although inexpensive storage is conducive to maintaining said data, accessing and managing it for knowledge discovery and data mining becomes a performance issue when datasets are large, dynamic, and distributed. In this work, we present our vision of a software framework consisting of middleware services to support interactive data mining over dynamic data at data analysis centers built on top of heterogeneous clusters. The design of a sampling service for dynamic data, together with initial performance results, are also presented.
13939828
Mutual information regularized Bayesian framework for multiple image restoration
Bayesian methods have been extensively used in various applications. However, there are two intrinsic issues rarely addressed, namely generalization and validity, in the context of multiple image restoration, we show that traditional Bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. To improve the Bayesian framework's generalization, we propose to explicitly enforce the validity of the result. Independent noise prior is very important but largely under-utilized in previous literature. In this paper, we use mutual information (MI) to explicitly enforce the independence. Efficient approximations based on Taylor expansion are proposed to adapt MI into standard energy forms to regularize the Bayesian methods. The new regularized Bayesian framework effectively utilizes the traditional generative signal/noise models but is much more robust to various model errors, as demonstrated in experiments on some demanding imaging applications.
783838
Practical Indexing XML Document for Twig Query
Answering structural queries of XML with index is an important approach of efficient XML query processing. Among existing structural indexes for XML data, F&B index is the smallest index that can answer all branching queries. However, an F&B index for less regular XML data often contains a large number of index nodes, and hence a large amount of main memory. If the F&B index cannot be accommodated in the available memory, its performance will degrade significantly. This issue has practically limited wider application of the F&B index. 
 
In this paper, we propose a disk organization method for the F&B index which shift part of the leave nodes in the F&B index to the disk and organize them judiciously on the disk. Our method is based on the observation that the majority of the nodes in a F&B index is often the leaf nodes, yet their access frequencies are not high. 
 
We select some leaves to output to disk. With the support of reasonable storage structure in main memory and in disk, we design efficient query processing method). We further optimize the design of the F&B index based on the query workload. Experimental results verified the effectiveness of our proposed approach.
7426756
Content-based language models for spoken document retrieval
Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multimedia collections in the near future. This paper presents a novel concept of applying the content-based language models to spoken document retrieval. In an example task for retrieval of Mandarin broadcast news, the content-based language models either trained with the automatic transcriptions of the spoken documents or adapted from the baseline language models using the automatic transcriptions of the spoken documents were used to create the more accurate recognition results and indexing terms from both the spoken documents and the speech queries. We report on some interesting findings obtained in this research.
29758508
Corner Detection Based on Finding Edge Points Locally