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Database queries can contain multiple predicates. The optimization of conjunctive predicates is still vital to the overall performance of analytic data processing tasks. Prior work proposes several memory-efficient storage layouts, e.g., BitWeaving and ByteSlice, to significantly accelerate predicate evaluation, as circuit-level intra-cycle parallelism available in modern CPUs can be exploited such that the total number of instructions can be dramatically reduced. However, the performance potential of conjunctive predicates has not been harvested yet under such storage layouts as there is no accurate cost model to provide necessary insights that guide the optimization process. In this paper, we propose a hybrid empirical/analytical cost model (Understanding) to unveil the performance characteristics of such storage layouts when applying to predicate evaluation. Our cost model takes into account effect of non-linear factors, e.g., cache miss and branch misprediction, and easily applies to different CPUs. The main finding from our cost model is to distinguish high-cost instruction (which suffers from cache miss and/or branch misprediction) from low-cost instruction (which enjoys cache hit and correct branch prediction) in the context of predicate evaluation under these storage layouts. Guided by such a finding, we propose a simple execution scheme Hebe (Optimizing), which is order-oblivious while maintaining high performance. Hebe is attractive to the query optimizer (QO), as the QO does not need to go through a sampling process to decide the optimal evaluation order in advance. The intuition behind Hebe is to significantly reduce the number of high-cost instructions while keeping low-cost instructions unchanged. Our finding from Hebe sheds light on the importance of accurate cost model that guide us to derive an efficient execution scheme for query processing on modern CPUs. [ABSTRACT FROM AUTHOR]

In many applications, local or remote sensors send in streams of data, and the system needs to monitor the streams to discover relevant events/patterns and deliver instant reaction correspondingly. An important scenario is that the incoming stream is a continually appended time series, and the patterns are time series in a database. At each time when a new value arrives (called a time position), the system needs to find, from the database, the nearest or near neighbors of the incoming time series up to the time position. This paper attacks the problem by using Fast Fourier Transform (FFT) to efficiently find the cross correlations of time series, which yields, in a batch mode, the nearest and near neighbors of the incoming time series at many time positions. To take advantage of this batch processing in achieving fast response time, this paper uses prediction methods to predict future values. When the prediction length is long, FFT is used to compute the cross correlations of the predicted series (with the values that have already arrived) and the database patterns, and to obtain predicted distances between the incoming time series at many future time positions and the database patterns. If the prediction length is short, the direct computation method is used to obtain these predicted distances to avoid the overhead of using FFT. When the actual data value arrives, the prediction error together with the predicted distances is used to filter out patterns that are not possible to be the nearest or near neighbors, which provides fast responses. Experiments show that with reasonable prediction errors, the performance gain is significant. Especially, when the long term predictions are available, the proposed method can handle incoming data at a very fast streaming rate. [ABSTRACT FROM AUTHOR]

Individual privacy will be at risk if a published data set is not properly deidentified. κ-Anonymity is a major technique to deidentify a data set. Among a number of κ-anonymization schemes, local recoding methods are promising for minimizing the distortion of a κ-anonymity view. This paper addresses two major issues in local recoding κ-anonymization in attribute hierarchical taxonomies. First, we define a proper distance metric to achieve local recoding generalization with small distortion. Second, we propose a means to control the inconsistency of attribute domains in a generalized view by local recoding. We show experimentally that our proposed local recoding method based on the proposed distance metric produces higher quality κ-anonymity tables in three quality measures than a global recoding anonymization method, Incognito, and a multidimensional recoding anonymization method, Multi. The proposed inconsistency handling method is able to balance distortion and consistency of a generalized view. [ABSTRACT FROM AUTHOR]

With the availability of affordable sensors and sensor networks, sensor-based human-activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particularly important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly, when the collected sensor data are biased toward normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce the false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between the abnormality detection rate and false alarm rate and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting. [ABSTRACT FROM AUTHOR]

Establishing an appropriate semantic overlay on peer-to-peer (P2P) networks to obtain both semantic ability and scalability is a challenge. Current DHT-based P2P networks are limited in their ability to support a semantic search. This paper proposes the Distributed Suffix Tree (DST) overlay as the intermediate layer between the DHT overlay and the semantic overlay to support the search of a keyword sequence. Its time cost is sublinear with the length of the keyword sequence. Analysis and experiments show that the DST-based search is fast, load-balanced, and useful in realizing an accurate content search on P2P networks. [ABSTRACT FROM AUTHOR]

Data stream management systems need to adaptively control their resources, since stream characteristics and query workload may vary over time. In this paper, we investigate an approach to adaptive resource management for continuous sliding- window queries that adjusts window sizes and time granularities to keep resource usage within bounds. These two novel techniques differ from standard load shedding approaches based on sampling, as they ensure exact query answers for given user-defined quality of service specifications, even under query reoptimization. In order to quantify the effects of both techniques on the various operations in a query plan, we develop an appropriate cost model for estimating operator resource allocation in terms of memory usage and processing costs. A thorough experimental study not only validates the accuracy of our cost model but also demonstrates the efficacy and scalability of the proposed techniques. [ABSTRACT FROM AUTHOR]

An important conceptual modeling activity in the development of database, object-oriented and agent-oriented systems is the capture and expression of domain constraints governing underlying data and object states. UML is increasingly used for capturing conceptual models, as it supports conceptual modeling of arbitrary domains, and has extensible notation allowing capture of invariant constraints both in the class diagram notation and in the separately denoted OCL syntax. However, a need exists for increased formalism in constraint capture that does not sacrifice ease of use for the analyst. In this paper, we codify a set of invariant patterns formalized here for capturing a rich category of propositional constraints on class diagrams. We use tools of Boolean logic to set out the distinction between these patterns, applying them in modeling by way of example. We use graph notation to systematically uncover constraints hidden in the diagrams. We present data collected from applications across different domains, supporting the importance of "pattern-finding" for n-variable propositional constraints using general graph theoretic methods. This approach enriches UML-based conceptual modeling for greater completeness, consistency, and correctness by formalizing the syntax and semantics of these constraint patterns, which has not been done in a comprehensive manner before now. [ABSTRACT FROM AUTHOR]

Given an object q, modeled by a multidimensional point, a reverse nearest neighbors (RNN) query returns the set of objects in the database that have q as their nearest neighbor. In this paper, we study an interesting generalization of the RNN query, where not all dimensions are considered, but only an ad hoc subset thereof. The rationale is that 1) the dimensionality might be too high for the result of a regular RNN query to be useful, 2) missing values may implicitly define a meaningful subspace for RNN retrieval, and 3) analysts may be interested in the query results only for a set of (ad hoc) problem dimensions (i.e., object attributes). We consider a suitable storage scheme and develop appropriate algorithms for projected ANN queries, without relying on multidimensional indexes. Given the significant cost difference between random and sequential data accesses, our algorithms are based on applying sequential accesses only on the projected atomic values of the data at each dimension, to progressively derive a set of RNN candidates. Whether these candidates are actual RNN results is then validated via an optimized refinement step. In addition, we study variants of the projected RNN problem, including RkNN search, bichromatic RNN, and ANN retrieval for the case where sequential accesses are not possible. Our methods are experimentally evaluated with real and synthetic data. [ABSTRACT FROM AUTHOR]

The recent Maximum Weighted Likelihood (MWL) [18], [19] has provided a general learning paradigm for density-mixture model selection and learning, in which weight design, however, is a key issue. This paper will therefore explore such a design, and through which a heuristic extended Expectation-Maximization (X-EM) algorithm is presented accordingly. Unlike the EM algorithm [1], the X-EM algorithm is able to perform model selection by fading the redundant components out from a density mixture, meanwhile estimating the model parameters appropriately. The numerical simulations demonstrate the efficacy of our algorithm. [ABSTRACT FROM AUTHOR]

Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database and from corpus statistics. The use of a lexical database enables our method to model human common sense knowledge and the incorporation of corpus statistics allows our method to be adaptable to different domains. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition. [ABSTRACT FROM AUTHOR]

The Knowledge Grid needs to operate with a scalable platform to provide large-scale intelligent services. A key function of such a platform is to efficiently support various complex queries in a dynamic large-scale network environment. This paper proposes a platform to support index-based path queries by incorporating a semantic overlay with an underlying structured P2P network that provides object location and management services. Various distributed indexing structures can be dynamically formed by publishing semantic objects as indexing nodes. Queries are forwarded along the chains of semantic object pointers to search for objects. We investigate the deployment of a scalable distributed trie index for broadcast queries on key strings, propose a decentralized load balancing method for solving the problem of uneven load distribution incurred by heterogeneity of loads and node capacities and by the distributed trie index, and give an approach for improving the availability of the semantic overlay and its trie index. Experiments demonstrate the scalability of the proposed platform. [ABSTRACT FROM AUTHOR]

Prefetching is an effective method for minimizing the number of fetches between the client and the server in a database management system. In this paper, we formally define the notion of prefetching. We also formally propose new notions of the type-level access locality and type-level access pattern. The type-level access locality is a pheonomenon that repetitive patterns exist in the attributes referenced. The type-level access pattern is a pattern of attributes that are referenced in accessing the objects. We then develop an efficient capturing and prefetching policy based on this formal framework. Existing prefetching methods are based on object-level or page-level access patterns, which consist of object-ids or page-ids of the objects accessed. However, the drawback of these methods is that they work only when exactly the same objects or pages are accessed repeatedly. In contrast, even though the same objects are not accessed repeatedly, our technique effectively prefetches objects if the same attributes are referenced repeatedly, i.e., if there is type-level access locality. Many navigational applications in Object-Relational Database Management Systems (ORDBMSs) have type-level access locality. Therefore, our technique can be employed in ORDBMSs to effectively reduce the number of fetches, thereby significantly enhancing the performance. We also address issues in implementing the proposed algorithm. We have conducted extensive experiments in a prototype ORDBMS to show effectiveness of our algorithm. Experimental results using the OO7 benchmark, a real GIS application, and an XML application show that our technique reduces the number of fetches by orders of magnitude and improves the elapsed time by several factors over on-demand fetching and context-based prefetching, which is a state-of-the-art prefetching method. These results indicate that our approach provides a new paradigm in prefetching that improves performance of navigational applications significantly and is a practical method that can be implemented in commercial ORDBMSs. [ABSTRACT FROM AUTHOR]

Existing data analysis techniques have difficulty in handling multidimensional data. Multidimensional data has been a challenge for data analysis because of the inherent sparsity of the points. In this paper, we first present a novel data preprocessing technique called shrinking which optimizes the inherent characteristic of distribution of data. This data reorganization concept can be applied in many fields such as pattern recognition, data clustering, and signal processing. Then, as an important application of the data shrinking preprocessing, we propose a shrinking-based approach for multidimensional data analysis which consists of three steps: data shrinking, cluster detection, and cluster evaluation and selection. The process of data shrinking moves data points along the direction of the density gradient, thus generating condensed, widely-separated clusters. Following data shrinking, clusters are detected by finding the connected components of dense cells (and evaluated by their compactness). The data-shrinking and cluster-detection steps are conducted on a sequence of grids with different cell sizes. The clusters detected at these scales are compared by a cluster- wise evaluation measurement, and the best clusters are selected as the final result. The experimental results show that this approach can effectively and efficiently detect clusters in both low and high-dimensional spaces. [ABSTRACT FROM AUTHOR]

In this paper, we show how the data envelopment analysis (DEA) model might be useful to screen training data so a subset of examples that satisfy monotonicity property can be identified. Using real-world health care and software engineering data, managerial monotonicity assumption, and artificial neural network (ANN) as a forecasting model, we illustrate that DEA-based data screening of training data improves forecasting accuracy of an ANN. [ABSTRACT FROM AUTHOR]