14092175
Query Languages and Data Models for Database Sequences and Data Streams
We study the fundamental limitations of relational algebra (RA) and SQL in supporting sequence and stream queries, and present effective query language and data model enrichments to deal with them. We begin by observing the well-known limitations of SQL in application domains which are important for data streams, such as sequence queries and data mining. Then we present a formal proof that, for continuous queries on data streams, SQL suffers from additional expressive power problems. We begin by focusing on the notion of nonblocking (NB) queries that are the only continuous queries that can be supported on data streams. We characterize the notion of nonblocking queries by showing that they are equivalent to monotonic queries. Therefore the notion of NB-completeness for RA can be formalized as its ability to express all monotonic queries expressible in RA using only the monotonic operators of RA. We show that RA is not NB-complete, and SQL is not more powerful than RA for monotonic queries. 
 
To solve these problems, we propose extensions that allow SQL to support all the monotonic queries expressible by a Turing machine using only monotonic operators. We show that these extensions are (i) user-defined aggregates (UDAs) natively coded in SQL (rather than in an external language), and (ii) a generalization of the union operator to support the merging of multiple streams according to their timestamps. These query language extensions require matching extensions to basic relational data model to support sequences explicitly ordered by times-tamps. Along with the formulation of very powerful queries, the proposed extensions entail more efficient expressions for many simple queries. In particular, we show that nonblocking queries are simple to characterize according to their syntactic structure.
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.
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.
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.
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.
15109806
Design of a next generation sampling service for large scale data analysis applications
Advances in data collection and storage technologies have resulted in large and dynamically growing data sets at many organizations. Database and data mining researchers often use sampling with great effect to scale up performance on these data sets with small cost to accuracy. However, existing techniques often ignore the cost of computing a sample. This cost is often linear in the size of the data set, not the sample, which is expensive. Furthermore, for data mining applications that leverage progressive sampling or bootstrapping-based techniques, this cost can be prohibitive, since they require the generation of multiple samples.To address this problem, we present a solution in the context of a state-of-the-art data analysis center. Specifically, we propose a scalable service that supports sample generation with cost linear in the size of the sample. We then present an efficient parallelization of this service. Our solution leverages high speed interconnects (e.g. Myrinet, Infini-band) for parallel I/O operations with pipelined data transfers. We export an interface that supports both ad-hoc SQL-like querying for database applications, as well as a stand-alone service for data mining applications. We then evaluate our work using queries abstracted from a network monitoring and analysis application, which uses both database and progressive sampling queries. We demonstrate that our implementation achieves good load balance and realizes up to an order of magnitude speedup when compared with extant approaches.
1542177
Out-of-core tensor approximation of multi-dimensional matrices of visual data
Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.
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.
2880184
Broadcast anti-jamming systems
In a traditional anti-jamming system a transmitter who wants to send a signal to a single receiver spreads the signal power over a wide frequency spectrum with the aim of stopping a jammer from blocking the transmission. In this paper, we consider the case that there are multiple receivers and the transmitter wants to broadcast a message to all receivers such that colluding groups of receivers cannot jam the reception of any other receiver. We propose efficient coding methods that achieve this goal and link this problem to well-known problems in combinatorics. We also link a generalisation of this problem to the Key Distribution Pattern problem studied in combinatorial cryptography.
41722021
Correction of bias for motion estimation algorithms
Ego motion can be estimated by epipolar equations from point correspondences using statistically unbiased method. Such technique involves a positive weight being added to the fraction of residuals. The side effect of introduction of positive weights is that additional computation is required. We propose three algorithms: Two are derived from the statistically unbiased method, the third one has been derived based on minimising the error defined as distance between the image point and the corresponding epipolar line. It is proven that the errors are Gaussian distributed and comparable performance is achieved without the positive weights. Verified with experiments using real and synthetic data, the findings agree with the theory and the new algorithms outperform analytical solutions.