17442336
Ordinal association rules for error identification in data sets
A new extension of the Boolean association rules, ordinal association rules, that incorporates ordinal relationships among data items, is introduced. One use for ordinal rules is to identify possible errors in data. A method that finds these rules and identifies potential errors in data is proposed.
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
1161117
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
We introduce a new model of similarity of time sequences that captures the intuitive notion that two sequences should be considered similar if they have enough non-overlapping time-ordered pairs of subsequences thar are similar. The model allows the amplitude of one of the two sequences to be scaled by any suitable amount and its offset adjusted appropriately. Two subsequences are considered similar if one can be enclosed within an envelope of a specified width drawn around the other. The model also allows non-matching gaps in the matching subsequences. The matching subsequences need not be aligned along the time axis. Given this model of similarity, we present fast search techniques for discovering all similar sequences in a set of sequences. These techniques can also be used to find all (sub)sequences similar to a given sequence. We applied this matching system to the U.S. mutual funds data and discovered interesting matches.
11135944
Fuzzy Clustering of Web Logs for User Classification
18594508
An index structure for efficient reverse nearest neighbor queries
The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new index structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single index structure is employed for a dynamic database, in contrast to the use of multiple indexes in previous work. This leads to significant savings in dynamically maintaining the index structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our index structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our index structure extremely preferable in both static and dynamic cases.
7539391
Compressed-domain video indexing techniques using DCT and motion vector information in MPEG video
Development of various multimedia applications hinges on the availability of fast and efficient storage, browsing, indexing, and retrieval techniques. Given that video is typically stored efficiently in a compressed format, if we can analyze the compressed representation directly, we can avoid the costly overhead of decompressing and operating at the pixel level. Compressed domain parsing of video has been presented in earlier work where a video clip is divided into shots, subshots, and scenes. In this paper, we describe key frame selection, feature extraction, and indexing and retrieval techniques that are directly applicable to MPEG compressed video. We develop a frame-type independent representation of the various types of frames present in an MPEG video in which al frames can be considered equivalent. Features are derived from the available DCT, macroblock, and motion vector information and mapped to a low-dimensional space where they can be accessed with standard database techniques. The spatial information is used as primary index while the temporal information is used to enhance the robustness of the system during the retrieval process. The techniques presented enable fast archiving, indexing, and retrieval of video. Our operational prototype typically takes a fraction of a second to retrieve similar video scenes from our database, with over 95% success.
11605665
Implementing a general real-time scheduling framework in the RED-Linux real-time kernel
Many scheduling paradigms have been studied for real-time applications and real-time communication network. Among them, the most commonly used paradigms include priority-driven, time-driven and share-driven paradigms. In this paper, we present a general scheduling framework which is designed to integrate these paradigms in one framework. The framework is implemented in our real-time extension of the Linux kernel, RED-Linux. Two scheduler components are used in the framework: Allocator and Dispatcher. For each job, the framework identifies four scheduling attributes: priority, start time, finish time and budget. We show that the framework can be used to efficiently implement many well-known scheduling algorithms. We also measure and analyze the performance of the framework implemented in RED-Linux.
13951248
A survey on evaluating and realizing IS/IT benefits in Taiwanese b2bec companies
A number of issues have emerged from the analysis of the data collected via a survey conducted in Taiwanese B2BEC companies. The results show relatively high usage of IS/IT investment evaluation and benefits realization methodologies, and yet, these methodologies were generally not used effectively within the responding organizations. Most of these organizations were not yet mature in terms of their IT. However, there was a clear association between level of IT maturity and both wide and effective use of methodologies for IS/IT investment evaluation and benefits management.
46090804
Integrating the fixed priority scheduling and the total bandwidth server for aperiodic tasks
The priority-based weighted fair queueing (PWFQ) schedule for real-time networks can be used to decouple session delay and its allocated bandwidth share. The idea is to introduce the concept of sliding window on the packet's virtual finish time. For packets whose finish time is within the window, the fixed priority assigned to each communication session is used to determine which packet should be sent out first. We apply the sliding window mechanism to aperiodic job scheduling with the total bandwidth server algorithm. When job arrivals are modeled by the leaky bucket model, we show that the algorithm can be used to improve the response time of aperiodic jobs.
15828117
Enhancing external consistency in real-time transactions
Real-time computing brings two new requirements to database management. The first is the deadline constraint which requires better scheduling algorithms to meet transaction deadfines. The second requirement is the temporal validity, or external consistency[Lin89], requirement of data: a real-time database must prevent data from being corrupted not only by the executions of concurrent transactions but also by the delay from sharing computing resources and real-time scheduler decisions. In this paper, we study the external consistency requirement in real-time databases. We propose a semantic-based concurrency control scheme that prefers the external consistency to the traditional serializability. Two new concepts are introduced in our work. An update of a physical object value is considered to be compatible to those read operations that need to access the most recent data. Secondly, if a transaction depends strongly on the validity of the value of an object, a new update may cause the transaction to be aborted since continuing the transaction may be harmful to the appfication. A real-Life example is the stock market database. A trading program may be executed for a long time and its read operations should not block any new updates. Moreover, if there is a big sudden change in a particular stock price, it may be better to abort an on-going transaction rather than letting it make some invalid decisions.
13196530
Amino Acid Substitution Matrices from an Artificial Neural Network Model
An amino acid substitution matrix specifies probabilities of substitutions for each pair of the 20 amino acids. Log-odds scores transformed from the values in substitution matrices are widely used to construct protein sequence alignments. Any given substitution matrix is suited to matching sequences diverged by a specific evolutionary distance. However, for a given set of sequences, it is not always clear what matrix should be used. We used an artificial neural network model to predict probabilities of amino acid substitutions with alignment samples of different evolutionary distances. From this internal description, substitution matrices suitable for detecting relationships at any chosen evolutionary distance can be instantly generated. By using the additional information of evolutionary distances, the average cross entropy error of our neural network model is lower than that of a series of BLOSUM and PET matrices over all testing sets. Our model is more accurate on the prediction of amino acid substitution probabilities.