5874117
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two challenges, the overwhelming volume of the streaming data, and the concept drifts. In this paper, we propose a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Beyesian, etc., from sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classification. Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.
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.
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.
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.
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.
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.
15384375
A hybrid source-channel coding scheme for object-based wireless video communications
In this paper, we study the joint source-channel coding of object-based video, and propose a data hiding scheme that improves the video error resilience by adaptively embedding the shape and motion information in the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on the knowledge of transmission channel conditions. The problem is solved using Lagrangian relaxation and dynamic programming. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection
14060305
Effectively Detecting All-Zero DCT Blocks for H.264 Optimization
This paper presents a novel efficient algorithm to reduce redundant DCT and quantization computations for H.264 encoding. A theoretical analysis is performed to study the sufficient condition for DCT coefficients to be quantized to zeros in H.264. As a result, a more tight sufficient condition is derived to early detect all-zero 4x4 DCT blocks. Compared with other algorithms, the proposed algorithm provides a more precise and efficient condition to predict all-zero DCT blocks. The experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of all the evaluated performances.
42852449
A Formal System of Logic
The main purpose of this paper is to present a formal system P in which we enjoy a smooth-running technique and which countenances a universe of classes which is symmetrical as between large and small. More exactly, P is a system which differs from the inconsistent system of [ 1 ] only in the introduction of a rather natural new restrictive condition on the defining formulas of the elements (sets, membership-eligible classes). It will be proved that if the weaker system of [ 2 ] is consistent, then P is also consistent. After the discovery of paradoxes, it may be recalled, Russell and Zermelo in the same year proposed two different ways of safeguarding logic against contradictions (see [ 3 ], [ 4 ]). Since then various simplifications and refinements of these systems have been made. However, in the resulting systems of Zermelo set theory, generation of classes still tends to be laborious and uncertain; and in the systems of Russell's theory of types, complications in the matter of reduplication of classes and meaningfulness of formulas remain. In [ 2 ], Quine introduced a system which seems to be free from all these complications. But later it was found out that in it there appears to be an unavoidable difficulty connected with mathematical induction. Indeed, we encounter the curious situation that although we can prove in it the existence of a class V of all classes, and we can also prove particular existence theorems for each of infinitely many classes, nobody has so far contrived to prove in it that V is an infinite class or that there exists an infinite class at all.
32652355
Browsing the Chinese Web Pages Using Mandarin Speech
A speech interface that allows easy access to information on the WWW has the potential to make the browser more friendly and powerful. This paper, thus, presents a working Mandarin speech interface for using unconstrained Mandarin speech to control the WWW browser for conveniently browsing the Chinese Web pages. The interface currently provides speakable commands, bookmarks, and links. The experimental results show that our approach which specially consider the characteristics of the Chinese language is very effective, and very high accuracy can be achieved.