10685141
Loadstar: A Load Shedding Scheme for Classifying Data Streams
We consider the problem of resource allocation in mining multiple data streams. Due to the large volume and the high speed of streaming data, mining algorithms must cope with the effects of system overload. How to realize maximum mining benefits under resource constraints becomes a challenging task. In this paper, we propose a load shedding scheme for classifying multiple data streams. We focus on the following problems: i) how to classify data that are dropped by the load shedding scheme? and ii) how to decide when to drop data from a stream? We introduce a quality of decision (QoD) metric to measure the level of uncertainty in classification when exact feature values of the data are not available because of load shedding. A Markov model is used to predict the distribution of feature values and we make classification decisions using the predicted values and the QoD metric. Thus, resources are allocated among multiple data streams to maximize the quality of classification decisions. Furthermore, our load shedding scheme is able to learn and adapt to changing data characteristics in the data streams. Experiments on both synthetic data and real-life data show that our load shedding scheme is effective in improving the overall accuracy of classification under resource constraints. keywords: data mining, data streams, load shedding, classification, quality of decision, feature prediction, Markov model.
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.
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.
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.
611504
Utilizing histogram information
Many commercial database management systems (e.g., DB2, Oracle, etc.) make use of histograms of the value distribution of individual attributes of relations in order to make good selections of query execution plans. These histograms contain partial information about the actual distribution, such as which attribute values occur most frequently, and how often each one occurs, and what value occurs at the kth quantile when the values are sorted.In this paper, we quantitatively assess the information gain (or uncertainty reduction) due to each of these types of histogram information both individually and in combination. Correspondingly, we observe how the accuracy of estimating frequencies of individual values improves with the availability of each of these types of histogram information. We suggest guidelines for constructing histograms tailored to each individual attribute depending on the characteristics of its attribute value distribution.
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.
11937214
A Query-by-Singing Technique for Retrieving Polyphonic Objects of Popular Music
This paper investigates the problem of retrieving popular music by singing. In contrast to the retrieval of MIDI music, which is easy to acquire the main melody by the selection of the symbolic tracks, retrieving polyphonic objects in CD or MP3 format requires to extract the main melody directly from the accompanied singing signals, which proves difficult to handle well simply using the conventional pitch estimation. To reduce the interference of background accompaniments during the main melody extraction, methods are proposed to estimate the underlying sung notes in a music recording by taking into account the characteristic structure of popular song. In addition, to accommodate users’ unprofessional or personal singing styles, methods are proposed to handle the inaccuracies of tempo, pause, transposition, or off-key, etc., inevitably existing in queries. The proposed system has been evaluated on a music database consisting of 2613 phrases extracted manually from 100 Mandarin pop songs. The experimental results indicate the feasibility of retrieving pop songs by singing.
794384
A Novel Intrusion Detection Method
It is an important issue for the security of network that how to detect new intrusions attack. This paper investigates unsupervised intrusion detection method. A distance definition for mixed attributes, a simple method calculating cluster radius threshold, a outlier factor measured deviating degree of a cluster, and a novel intrusion detection method are proposed in this paper. The experimental results show that the method has promising performance with high detection rate and low false alarm rate, also can detect new intrusion.