Semi-supervised Data Organization for Interactive Anomaly Analysis.

2006 ◽  
Author(s):  
Javed Aslam ◽  
Sergey Bratus ◽  
Virgil Pavlu
Keyword(s):  
Data Organization ◽  
Anomaly Analysis

A Survey on Database in Cloud Computing with Reference to the Traditional Database

2017 ◽  
Vol 7 (11) ◽  
pp. 150
Author(s):  
Eter Basar ◽  
Ankur Pan Saikia ◽  
L. P. Saikia
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
The Internet ◽  
Data Organization ◽  
Technology Industry ◽  
Business Applications ◽  
Business Information ◽  
Points Of Interest ◽  
Cloud Databases ◽  
Online Reservation

Data Technology industry has been utilizing the customary social databases for around 40 years. Be that as it may, in the latest years, there was a generous transformation in the IT business as far as business applications. Remain solitary applications have been supplanted with electronic applications, conferred servers with different proper servers and committed stockpiling with framework stockpiling. Lower expense, adaptability, the model of pay-as-you-go are the fundamental reasons, which caused the conveyed processing are transformed into reality. This is a standout amongst the hugest upsets in Information Technology, after the development of the Internet. Cloud databases, Big Table, Sherpa, and SimpleDB are getting the opportunity to be more natural to groups. They featured the hindrances of current social databases as far as convenience, adaptability, and provisioning. Cloud databases are basically utilized for data raised applications, for example, stockpiling and mining of gigantic information or business information. These applications are adaptable and multipurpose in nature. Various esteem based data organization applications, such as managing an account, online reservation, e-exchange and stock organization, and so on are delivered. Databases with the help of these sorts of uses need to incorporate four essential highlights: Atomicity, Consistency, Isolation, and Durability (ACID), in spite of the fact that utilizing these databases isn't basic for utilizing as a part of the cloud. The objective of this paper is to discover the points of interest and disservices of databases generally utilized in cloud frameworks and to survey the difficulties in creating cloud databases

Application of geometric moving average martingale algorithm in anomaly analysis before earthquake based on sliding window

2013 ◽  
Vol 33 (12) ◽  
pp. 3608-3610 ◽  
Author(s):  
Liping CHEN ◽  
Xiangzen KONG ◽  
Zhi ZHENG ◽  
Xinqi LIN ◽  
Xiaoshan ZHAN
Keyword(s):  
Moving Average ◽  
Sliding Window ◽  
Anomaly Analysis

scholarly journals Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2532
Author(s):  
Encarna Quesada ◽  
Juan J. Cuadrado-Gallego ◽  
Miguel Ángel Patricio ◽  
Luis Usero
Keyword(s):  
Anomaly Detection ◽  
Outlier Detection ◽  
Research Work ◽  
Industrial Sector ◽  
Sensor Data ◽  
Correct Selection ◽  
Wind Tunnels ◽  
Data Set ◽  
First Case ◽  
Anomaly Analysis

Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough—compared with the rest of the data set that is being analyzed—and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account. The correct selection and use of the model to be applied to a specific problem is fundamental for the success of the anomaly detection study and, in many cases, the use of only one model cannot provide sufficient results, which can be only reached by using a mixture model resulting from the integration of existing and/or ad hoc-developed models. This is the kind of model that is developed and applied to solve the problem presented in this paper. This study deals with the definition and application of an anomaly detection model that combines statistical models and a new method defined by the authors, the Local Transilience Outlier Identification Method, in order to improve the identification of outliers in the sensor-obtained values of variables that affect the operations of wind tunnels. The correct detection of outliers for the variables involved in wind tunnel operations is very important for the industrial ventilation systems industry, especially for vertical wind tunnels, which are used as training facilities for indoor skydiving, as the incorrect performance of such devices may put human lives at risk. In consequence, the use of the presented model for outlier detection may have a high impact in this industrial sector. In this research work, a proof-of-concept is carried out using data from a real installation, in order to test the proposed anomaly analysis method and its application to control the correct performance of wind tunnels.

Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks

2021 ◽  
Vol 15 (4) ◽  
pp. 1-22
Author(s):  
Huan Wang ◽  
Chunming Qiao ◽  
Xuan Guo ◽  
Lei Fang ◽  
Ying Sha ◽  
...  
Keyword(s):  
Social Networks ◽  
Structural Change ◽  
Structural Changes ◽  
Structural Information ◽  
Similarity Index ◽  
Dynamic Social Network ◽  
Dynamic Social Networks ◽  
Micro Level ◽  
Anomaly Analysis ◽  
Similarity Method

Recently, dynamic social network research has attracted a great amount of attention, especially in the area of anomaly analysis that analyzes the anomalous change in the evolution of dynamic social networks. However, most of the current research focused on anomaly analysis of the macro representation of dynamic social networks and failed to analyze the nodes that have anomalous structural changes at a micro level. To identify and evaluate anomalous structural change-based nodes in generalized dynamic social networks that only have limited structural information, this research considers undirected and unweighted graphs and develops a multiple-neighbor superposition similarity method ( ), which mainly consists of a multiple-neighbor range algorithm ( ) and a superposition similarity fluctuation algorithm ( ). introduces observation nodes, characterizes the structural similarities of nodes within multiple-neighbor ranges, and proposes a new multiple-neighbor similarity index on the basis of extensional similarity indices. Subsequently, maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, based on and , not only identifies anomalous structural change-based nodes by detecting the anomalous structural changes of nodes but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that can accurately identify anomalous structural change-based nodes and evaluate their anomalous degrees well.

scholarly journals Utilizing Interdisciplinary Strategies for Next Generation Ecosystem Experiments Tropics Data Organization

2019 ◽  
Author(s):  
Emily Robles ◽  
Deb Agarwal ◽  
Danielle Christianson ◽  
Boris Faybishenko ◽  
Robinson Negron-Juarez ◽  
...  
Keyword(s):  
Data Organization ◽  
Next Generation

Data Organization

1977 ◽  
Vol 19 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Bruce M. Durding ◽  
Curtis A. Becker ◽  
John D. Gould
Keyword(s):  
College Students ◽  
Information Retrieval ◽  
Organizational Structures ◽  
Semantic Relations ◽  
Data Organization ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Computer Based

Three experiments investigated how people organize data. Subjects were given sets of 15-20 words and asked to organize them on paper. Each word set had a pre-defined organization (hierarchy, network, lists, table) based on the semantic relations among the words. Experiment 1 showed that college students have all these organizational structures available for use. They organized most word sets on the basis of the semantic relations inherent in them. Whereas most subjects used “appropriate” organizations (those that most easily preserved the relations), a few subjects organized nearly all word sets into lists. Experiment 2 showed that subjects can efficiently fit the word sets into “skeletons” that were explicitly designed to maintain all the semantic relations among the words. Experiment 3 showed that subjects have difficulty in preserving the relations among the words when they were required to organize them into inappropriate structures. These results are evaluated relative to the use of computer-based information retrieval systems.

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