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The problem considered in this paper is regression with a constraint on the precision of each prediction in the framework of data streams subject to concept drifts (when the hidden distribution which generates the observations can change over time). Concept drifts can diminish the reliability of the predictions over time and it might not be possible to output a prediction which satisfies the constraints...
Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are sub-categories of single-label classification. Several classifiers are developed for binary, multi-class...
Sequential pattern mining is an interesting data mining problem with many real-world applications. Though, new applications introduce a new form of data called data stream, no study has been reported on mining sequential patterns from quantitative data stream. This paper presents a novel algorithm, for mining quantitative streams. The proposed algorithm can mine exact set of fuzzy sequential patterns...
In the automotive field, intelligent control systems and information services using vehicle data are rapidly expanding. However, there is no software architecture to handle sensor data in vehicles and their surrounding efficiently. To solve these problems, we have researched and developed data centric software architecture for automotive systems. We have also developed a data-stream management system...
This paper presents an innovative idea for the classification of individual drivers. The classification is based on each driver's driving features like, ratio of indicators to turns, number of brakes, number of time horn used, average gear, average speed, maximum speed and gear. K-means and hierarchical clustering is used to separate out the slow, normal and fast driving styles based on recorded data...
Falls are serious problem especially for elderly people. Day by day the elderly people are living alone and the children of these people want to get information in dangerous situations. With the alarm systems, someone in difficulty can be detected and emergency aid can be sent. We propose a system to detect falls by using a data mining approach on WSN data. The proposed system evaluated using data...
This paper discusses the random sampling algorithm for landmark windows over weighted streaming data, and presents a new algorithm by improving weighted random sampling (WRS) algorithm with a reservoir. When a new data item vi with weight wi arrives, a random number ui is generated, and a key ki is calculated by wi and ui for the data item. We maintain a candidate sample set by the keys of data items,...
In recent years, the management and processing of data streams has become a topic of active research in several fields of computer science, such as distributed systems, database systems, and data mining. In data streams' applications, such as network monitoring, telecommunication systems and sensor networks, because of online monitoring, answering to the user's queries should be time and space efficient...
For the important role of packets in the network management and security applications, many researches have been applied to network packet. How to record and store the packets in an efficient structure is a problem in this field. This paper focuses on the study of network packet tagging technique. Based on existing research, this paper presents a tagging method using the attributes of data stream...
Continuous Data Protection (CDP) techniques have received great interest both from academia and industry in recent years. Aiming at the challenges to design and implement a CDP system, this paper proposes GSP_BCDP: a novel continuous data protection architecture at block level. Compared with other approaches, it uses a global shared storage pool to actually store all the versions of data contents,...
Controlling the space consumption and improving the precision of mining result is two challenges of frequent patterns mining in data stream. The parameter ?? which denotes the maximum error is widely used to reduce the space consumption. In this paper, we firstly propose a computational strategy for identifying maximum error, consist of resource awareness and polynomial approximate, and then propose...
By considering different weights of the items, weighted frequent pattern (WFP)mining can discover more important knowledge compared to traditional frequent pattern mining. Therefore, WFP mining becomes an important research issue in data mining and knowledge discovery area. However, the existing algorithms cannot be applied for stream data mining because they require multiple database scans. Moreover,...
With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window...
Skyline computing has become a hot topic in the International since 2001. In data stream environment, previous works about Skyline computing only sought to maintain full space Skyline points or compute subspace Skyline points over sliding window. No one has considered the problem of computing constrained Skyline points over sliding window. For many real-word applications, however, users usually expect...
Mining data streams for frequent patterns is important in many applications. Unlike traditional static databases, the underlying process that generates the data streams evolves over time. Past data may become outdated and of little use when compared to the most recent one. When a significant change occurs, much harm is done to the mining result if it is not properly handled. In this paper, an online...
A new approach to fuzzy rule-based systems structure identification in on-line (possibly real-time) mode is described in this paper. It expands the so called evolving Takagi-Sugeno (eTS) approach by introducing self-learning aspects not only to the number of fuzzy rules and system parameters but also to the number of antecedent part variables (inputs). The approach can be seen as online sensitivity...
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