14675910
Generalized dimension-reduction framework for recent-biased time series analysis
Recent-biased approximations have received increased attention recently as a mechanism for learning trend patterns from time series or data streams. They have shown promise for clustering time series and incrementally pattern maintaining. In this paper, we design a generalized dimension-reduction framework for recent-biased approximations, aiming at making traditional dimension-reduction techniques actionable in recent-biased time series analysis. The framework is designed in two ways: equi-segmented scheme and vari-segmented scheme. In both schemes, time series data are first partitioned into segments and a dimension-reduction technique is applied to each segment. Then, more coefficients are kept for more recent data while fewer kept for older data. Thus, more details are preserved for recent data and fewer coefficients are kept for the whole time series, which improves the efficiency greatly. We experimentally evaluate the proposed approach, and demonstrate that traditional dimension-reduction techniques, such as SVD, DFT, DWT, PIP, PAA, and landmarks, can be embedded into our framework for recent-biased approximations over streaming time series.
28734493
AGRID: An Efficient Algorithm for Clustering Large High-Dimensional Datasets
The clustering algorithm GDILC relies on density-based clustering with grid and is designed to discover clusters of arbitrary shapes and eliminate noises. However, it is not scalable to large high-dimensional datasets. In this paper, we improved this algorithm in five important directions. Through these improvements, AGRID is of high scalability and can process large high-dimensional datasets. It can discover clusters of various shapes and eliminate noises effectively. Besides, it is insensitive to the order of input and is a non-parametric algorithm. The high speed and accuracy of the AGRID clustering algorithm was shown in our experiments.
5595688
Discovering Interesting Association Rules by Clustering
There are a great many metrics available for measuring the interestingness of rules In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications Experiments show the effectiveness of our algorithm.
38902380
Construction Algorithm of Principal Curves in the Sense of Limit
Principal curves have been defined as self-consistent, smooth, one-dimensional curves which pass through the middle of a multidimensional data set. They are nonlinear generalization of the first Principal Component. In this paper, we take a new approach by defining principal curves as continuous curves based on the local tangent space in the sense of limit. It is proved that this new principal curves not only satisfy the self-consistent property, but also are the unique existence for any given open covering. Based on the new definition, a new practical algorithm for constructing principal curves is given. And the convergence properties of this algorithm are analyzed. The new construction algorithm of principal curves is illustrated on some simulated data sets.
15310670
A Recent-Biased Dimension Reduction Technique for Time Series Data
There are many techniques developed for tackling time series and most of them consider every part of a sequence equally. In many applications, however, recent data can often be much more interesting and significant than old data. This paper defines new recent-biased measures for distance and energy, and proposes a recent-biased technique based on DWT for time series in which more recent data are considered more significant. With such a recent-biased technique, the dimension of time series can be reduced while effectively preserving the recent-biased energy. Our experiments have demonstrated the effectiveness of the proposed approach for handling time series.
16630001
Model-based Runtime Verification Framework for Self-optimizing Systems
This paper describes a novel on-line model checking approach offered as service of a real-time operating system (RTOS). The verification system is intended especially for self-optimizing component-based real-time systems where self-optimization is performed by dynamically exchanging components. The verification is performed at the level of (RT-UML) models. The properties to be checked are expressed by RT-OCL terms where the underlying temporal logic is restricted to either time-annotated ACTL or LTL formulae. The on-line model checking runs interleaved with the execution of the component to be checked in a pipelined manner. The technique applied is based on on-the-fly model checking. More specifically for ACTL formulae this means on-the-fly solution of the NHORNSAT problem while in the case of LTL the emptiness checking method is applied.
61999607
Towards unbiased end-to-end network diagnosis
Internet fault diagnosis is extremely important for end users, overlay network service providers (like Akamai [1]) and even Internet service providers (ISPs). However, because link-level properties cannot be uniquely determined from end-to-end measurements, the accuracy of existing statistical diagnosis approaches is subject to uncertainty from statistical assumptions about the network. In this paper, we propose a novel Least-biased End-to-end Network Diagnosis (in short, LEND) system for inferring link-level properties like loss rate. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. We also design efficient algorithms to find all the MILSes and infer their loss rates for diagnosis. Our LEND system works for any network topology and for both directed and undirected properties, and incrementally adapts to network topology and property changes. It gives highly accurate estimates of the loss rates of MILSes, as indicated by both extensive simulations and Internet experiments. Furthermore, we demonstrate that such diagnosis can be achieved with fine granularity and in near real-time even for reasonably large overlay networks. Finally, LEND can supplement existing statistical inference approaches and provide smooth tradeoff between diagnosis accuracy and granularity.
748152
On-line testing of multi-source noise-induced errors on the interconnects and buses of system-on-chips
With processors and system-on-chips using nano-meter technologies, several design and test efforts have been recently developed to eliminate and test for many emerging DSM (deep sub-micron) noise effects. In this paper, we show the emergence of multi-source noise effects, where multiple DSM noise sources combine to produce functional and timing errors even when each separate noise source itself does not. We show the dynamic nature of multi-source noise, and the need for on-line testing to detect such noise errors. We propose a double-sampling data checking based low-cost on-line error detection circuit to test for such noise effects in on-chip buses. Based on the proposed circuit, an effective and efficient testing methodology has been developed to facilitate online testing for generic on-chip buses. The applicability of this methodology is demonstrated through embedding the on-line detection circuit in a bus design. The validated design shows the effectiveness of the proposed testing methodology for multi-source noise-induced errors in global interconnects and buses.
15518950
Effective and Scalable Authorship Attribution Using Function Words
Techniques for identifying the author of an unattributed document can be applied to problems in information analysis and in academic scholarship. A range of methods have been proposed in the research literature, using a variety of features and machine learning approaches, but the methods have been tested on very different data and the results cannot be compared. It is not even clear whether the differences in performance are due to feature selection or other variables. In this paper we examine the use of a large publicly available collection of newswire articles as a benchmark for comparing authorship attribution methods. To demonstrate the value of having a benchmark, we experimentally compare several recent feature-based techniques for authorship attribution, and test how well these methods perform as the volume of data is increased. We show that the benchmark is able to clearly distinguish between different approaches, and that the scalability of the best methods based on using function words features is acceptable, with only moderate decline as the difficulty of the problem is increased.
36457558
Verification Framework for UML-Based Design of Embedded Systems
System level design incorporating system modeling and formal specification in combination with formal verification can substantially contribute to the correctness and quality of the embedded systems and consequently help reduce the development costs. Ensuring the correctness of the designed system is, of course, a crucial design criterion especially when complex distributed (realtime) embedded systems are considered. Therefore, this paper aims at presenting a verification framework designated for formal verification and validation of UML-based design of embedded systems. It first introduces an approach of using the AsmL language for acquiring formal models of the UML semantics and consequently presents an on-the-fly model checking technique designed to run the formal verification directly over those semantic models.