Top PDF The Quality Attribute Design Strategy for a Social Network Data Analysis System

The Quality Attribute Design Strategy for a Social Network Data Analysis System

The Quality Attribute Design Strategy for a Social Network Data Analysis System

Social media has become an important part of communication in modern society. Social networking sites have become an important platform which people communicate. Among them, micro-blogging is growing rapidly. The microblogging phenomenon began in 2006, and now millions of users’ microblogs generate massive content every day. Through mi- croblogging people know the latest news, learn new knowledge, and share their lives. Twit- ter is a well-known implementation of micro-blogging that started in April 2006. Twitter messages, called tweets, have a maximum length of 140 characters. Relationships between people with a Twitter account are unidirectional, meaning that one user can follow another, but the user who is followed does not need to follow back that user. All tweets are public by default, and interesting tweets can be retweeted so that the original tweet can reach a wider audience. Through those data and content, the scientists can analyze human behavior or emotions [3]. In todays field of scientific research, software can help scientists effectively analyze massive amounts of data, greatly reducing time and labor costs. There are many data analysis tools available to help researchers analyze huge amounts of social network data. Some of the tools (Gephi, Graphviz, etc.) can visualize those data and help re- searchers to understand the relationships among them [4]. In this thesis, we want to design a new social network data analysis tool, named Trowser and based on Twitter data. The main goal of Trowser is to help researchers to handle geographic information and Twitter data.
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Design and analysis of a general data evaluation system based on social networks

Design and analysis of a general data evaluation system based on social networks

Elons et al. [8] propose a hybrid method of stochastic population-based search algorithm (particle swarm optimization), and a gradient-based algorithm (back propagation) is used to train a multiplicative neural net- work. The proposed network topology and training algo- rithms have shown superiority on the traditional neural network for determining the optimal drilling path. The proposed system generated proposed drilling plans that achieved more than 88% drilling decision accuracy which is measured according to the actual drilling path. Predict- ing the stock price is considered the most challenging and important financial topic. Reference [9] proposes a hybrid ensemble model based on BP neural network and EEMD to predict FTSE100 closing price. There are five hybrid prediction models, EEMD-NN, EEMD-bagging-NN, EEMD-cross validation-NN, EEMD-CV-bagging-NN, and EEMD-NN-proposed method. Experimental result shows that EEMD-bagging-NN, EEMD-cross validation-NN, and EEMD-CV-bagging-NN models performance are a notch above EEMD-NN and significantly higher than the single- NN model. And EEMD-NN-proposed method ’ s predic- tion performance superiority is demonstrated compared with the all presented models and was feasible and effect- ive in predicting the FTSE100 closing price. As a result of the significant performance of the proposed method, the method can be utilized to predict other financial time series data. Zhang et al. [10] consider a new hot-rolled strip thickness model prediction method based on ex- treme learning machine (ELM). The network uses live production data for training and testing, compared with the BP network prediction model. Simulation results show that the model can predict the thickness more quickly and accurately and is able to meet the needs of actual rolling production.
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Towards a Strategy Design Method for Corporate Data Quality Management

Towards a Strategy Design Method for Corporate Data Quality Management

At first the enterprise needs to decide on the basic landscape, which leads to the question which data should be managed on a corporate level and which on a regional or local level. In many cases the trigger for initiating a CDQM program was the deci- sion for a central application system architecture by the future sponsor on executive level. Once the executive sponsor has assigned the CDQM mandate and set the over- all CDQM objectives, then the CDQM team needs to analyze the core data objects (e.g. what is an “active” customer) and define the conceptual data model. In parallel or slightly delayed the data lifecycle of the previously identified core data objects is analyzed and redesigned (first for a pilot domain and then rolled out for other do- mains). At the same time when designing the data lifecycle of the data objects the roles and responsibilities for the data owners and data stewards can be specified. The Chief Data Steward then has to establish CDQM committees and integrate them into the existing network of committees and processes. The data quality controlling e.g. for a specific data class such as material data can begin as soon as the target data ar- chitecture and the data lifecycle for the data objects are finalized. At the same time systems are analyzed and designed, which support the data architecture (storage and distribution, meta data management) as well as the data life-cycle (e.g. workflows).
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MEASUREMENT OF STUDENT LEARNER’S ATTRIBUTE IN ONLINE COLLABORATIVE PROBLEM SOLVING USING SOCIAL NETWORK ANALYSIS

MEASUREMENT OF STUDENT LEARNER’S ATTRIBUTE IN ONLINE COLLABORATIVE PROBLEM SOLVING USING SOCIAL NETWORK ANALYSIS

Social network analysis (SNA) is the methodical analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.) [7]. The researcher present details about the design approach for analyzing small -medium and dynamic social network. The proposed study approach is to designed on a standard architecture and it can be applied on a broad range of social network metrics and social network analysis techniques. Based on common knowledge of system design, intuitively the researcher believe that the approach will provide important advantages for small-medium social network analysis, such as accelerating the analyses process, imparting a number of stages of analysis results, efficiently dealing with modal graphs. In order to consider and validate the approach, the researcher figure out to find out about the methodology’s performance, both theoretically and experimentally, on a set of SNA issues which cover a vast range of difficulties. According to application importance, the researcher decides to select the following four SNA metrics: Degree centrality centers on the sum of communication movement, decide the centrality of a node by means of its degree [8]. Usually the plain to degree the centrality because it simply signifies the figure of a network member’s direct contacts and has the advantage of being moderately simple to translate and communicate [9]. Degree centrality. Degree centrality equals to the figure of ties that a vertex has with other vertices. The equation where ) is the degree of as shown in the equation (1):
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Social Network Analysis on Educational Data Set in RDF Format

Social Network Analysis on Educational Data Set in RDF Format

The manager has the right to run social network analysis on all the data collected by the edu- cational platform, conversely, the tutor’s access has to be limited to the courses that he teaches. One approach is to limit access to RDF data based on the user privileges defined in the ed- ucational platform by mapping and converting the user privileges to RDF. The privileges in RDF format can be used with an access con- trol specification language to enforce the ac- cess rules ( Costabello et al., 2012; Flouris et al., 2010 ) . For this method to work, validating the user identity has to be done using a login procedure. This is not currently available in the Gephi tool set, so an extra plug-in needs to be developed. The second approach is to rely on the access control module from the educa- tional platform to confirm the user and check the rights of the tutor. For this, a module for the educational platform needs to be developed that can query the RDF endpoints and provide visualization of the social interaction graph. On the other hand, interpreting the social inter- action graph needs prior knowledge, and some skills are required to run the analysis using a specialized tool like Gephi. The manager is more suited to use such a tool. For the teacher, however, the analysis must be run in an auto- matic way and some interpretation of the social graph must be presented. This suggests that the best approach to provide access to social net- work analysis to the tutor is to build a module for the educational platform. In this way, one addresses the concerns regarding access control and the skills required to run SNA.
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A Data Security Self-Attribute System in Cloud Computing

A Data Security Self-Attribute System in Cloud Computing

To start with, SSDD does not consider the issue of the desired release time of the sensitive information the close time of both SSDD and FullPP Second, SSDD and many different plans are dependent on the perfect presumption of "No attacks on VDO (vanishing data object) before it terminates" . Third, it is exhibited that the Vanish plan is vulnerable against the Sybil attacks from the DHT network, the SSDD scheme and different plans are comparable. Therefore, indicating that the encoded information thing must be decrypted between The information owner encrypts his/her information to share to clients in the framework, in which each client’s key is related with a get to tree and each leaf node is related with a time moment.
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Design Process of a Social Network System for Storage and Share Files in the Workplace

Design Process of a Social Network System for Storage and Share Files in the Workplace

Not all the participants use mobile phones for work purpose, but they mentioned interest of using it if the company gave them the device. The ones who use mobile phones for work, they usually review, edit and share fi les from their devices. What are the Drawbacks and Opportunities to Improve Sharing and Storage Practices in the Workplace? It is clear that participants use numerous software alternatives to share and store fi les in the workplace. And also the residual space in their devices might be availed. The study suggested an integration of tools to share and storage fi les in the workplace. Additionally, it is clear that social network structure is used for safety and recovers fi les. In order to recover fi les, they ask co-workers to send them last versions, in case they do not have anymore. Even though, most of them have personalized way to organized fi les in their computer, most of the interviews already share a server space with their team to save fi les. The main problems are many similar versions of fi les available, each team member store in their machines. Moreover, not always they are aware of who has the updated version. Furthermore, some participants have not only the computer machine but also mobile phones for work purposes, which gives room for use the unused space of those devices and avoiding servers.
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Social Network Analysis using Data Segmentation and Neural Networks

Social Network Analysis using Data Segmentation and Neural Networks

So, in order to extract information related to various attributes that we have selected, we need to analyze the feed and social activity of the person. On the basis of this we can quantify the various qualitative features so as they can be used as an input to the neural network. So, what we do is that for any particular attribute A, we have a set of classes in which the person can be classified. To find out which class of the attribute a person belongs, we extract certain a number of keywords from the social media activity of a person which can be done with any keyword extraction algorithm. Now, these keywords serve as the basis to classify the particular person into a particular category of the attribute.Since, we can find a predefined set of keywords for each of these classes, using the pre-trained set of word vectors obtained from Word2Vec, we can find the cosine similarity between any two set of keywords. Hence, to find the overall similarity between the set of keywords obtained from the activity of the person, and the keywords of each class of attributes, we define a score to find the average simila ity between the two sets of keywo ds of ‘m’ classes of the att ibutes and let R be the set of keywo ds extracted from the social media activity of a person, also S ij represents the j th keyword of the i^th class of the
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Network Data Security System Design with High Security Insurance

Network Data Security System Design with High Security Insurance

The Nios II processor supports user-defined custom instructions as well as hardware acceleration. It can also perform other functions such as processing, controlling, decision making, and ordering. These features allow the software designer to write the control flow program in C/C++, while most of the computing is done in hardware. The software application includes a custom instruction that operates as a high-speed, hardware-accelerated computing module. In addition to supporting custom instructions, the Nios II Integrated Development Environment (IDE), which includes the GNU C/C++ assembler and Eclipse IDE, is ideal for the entire design process. Furthermore, the designer can perform simulation, development, debugging, integration, and validation in real time on the Development and Education (DE2) board. All of these features make development efficient.
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Application of Social Network Analysis for Livelihood System Study

Application of Social Network Analysis for Livelihood System Study

stock of resources used to generate well-being (Rakodi, 1999). Assets include human capital (age, education and training, and family structure); natural capital (e.g., climate, water and land); physical capital (equipment, livestock, and electricity); financial assets (credit); location-specific factors (such as access to infrastructure and social services), and social, political, and institutional assets, including social and political networks, and social inclusion. A livelihood framework is a way of understanding how households derive their livelihoods. An easy way of thinking within a livelihood framework is using the household triangle of assets, capabilities and activities. Household members use their capabilities and assets to carry out activities for sustaining livelihoods. Household assets refer to the resources that households own or have access to for gaining a livelihood. Where capabilities are the combined knowledge, skills, state of health and ability to labour or command labour of a household. Household strategies are the ways in which households deploy assets, use their capabilities in order to meet households’ objectives, and are often based on past experience. In the present article, we use livelihood framework to understand livelihood system. Hence, the terms ‘livelihood framework’ and ‘livelihood system’ are used synonymously for operational purposes.
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Data Analysis on Social Network Media Data like          Twitter Using Sentiment Analysis

Data Analysis on Social Network Media Data like Twitter Using Sentiment Analysis

We conclude by saying that, our work can be used by any company or industry to conduct a survey about their products so as to determine the statistics of their business. Customer analysis can be done to improvise their business.Can be applicable to movie Review-related websites such as movie reviews, product reviews etc.Individual user can also use it to know the sentiment about a particular product, topic or politics.The usage of latest and updated NLTK classifiers improve our accuracy and more over we improve the training set to get more accurate results.

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Toward Quality Attribute Driven Approach to Software Architectural Design

Toward Quality Attribute Driven Approach to Software Architectural Design

business concerns, and meeting the required cost and scheduling, optimal use of manpower, and so on. Having a structured method helps to ensure questioning in the initial requirement and design stages when discovered problems can be solved in an inexpensive manner. It guides users about the method—the stake- holders—to look for conflicts in the requirements and for resolutions of these conflicts in the software architecture. In realizing the method, assumption is that attribute-specific analyses are interdependent, and that each quality attribute has connections with other attributes, through specific architectural elements. An architectural element is a component, a property of the component, or a prop- erty of the relationship between components that affects some quality attribute. For example, the priority of a process is an architectural element that could af- fect performance. The tradeoff analysis, often regarded as trade off points, helps to identify these interdependencies [3].
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Social Network Data Analysis for User Stress Discovery and Recovery

Social Network Data Analysis for User Stress Discovery and Recovery

The rise of social media is changing people’s life, as well as research in healthcare and wellness with the development of social networks like Twitter and Sina Weibo, more and more people are willing to share their daily events and moods, and interact with friends through the social networks. As these social media data timely reflect users’ real-life states and emotions in a timely manner, it offers new opportunities for representing, measuring, modeling, and mining users behavior patterns through the large- scale social networks, and such social information can find its theoretical basis in psychology research. For example, found that stressed users are more likely to be socially less active, and more recently, there have been research efforts on harnessing social media data for developing mental and physical healthcare tools. For example, proposed to leverage Twitter data for real-time disease surveillance; while tried to bridge the vocabulary gaps between health seekers and providers using the community generated health data. There are also some research works using user tweeting contents on social media platforms to detect users’ psychological stress. Existing works [1] demonstrated that leverage social media for healthcare, and in particular stress detection, is feasible.
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An Improved Rumor Inhibition Strategy in Social Network

An Improved Rumor Inhibition Strategy in Social Network

Among the current rumor inhibition strategies, the common characteristic was that, inhibition action started in early period of rumor propagation. In this proposition, most inhibition strategies could work efficiently and obtain ideal performance. However, the effectiveness of most strategies was limited at a later stage of rumor propagation. As an example, the Hub nodes in objective immunization in [5], became probably infective nodes at later stage of rumor propagation because of node degree. Furthermore, nodes in social network usually belong to one or more communities and spread naturally information among several communities. Moreover, most strategies selected randomly initial immune node, and neglected the influence of initial immune node to the rumor inhibition. Aiming at the problems mentioned above, a multi-community immunization in social network was suggested and comparatively analyzed with other rumor inhibition strategies.
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Data Quality Strategy a guide. Helping you to build a contact data management strategy

Data Quality Strategy a guide. Helping you to build a contact data management strategy

g) shall be subject to appropriate technical and organisational measures against unauthorised or unlawful processing of personal data and against accidental loss or destruction of, or damage to, personal data; and h) shall not be transferred to a country or territory outside the European Economic Area unless that country or territory ensures an adequate level of protection for the rights and freedoms of data subjects in relation to the processing of personal data. Data Measures

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Data Center Network Design

Data Center Network Design

Management and configuration: managing an DCN with an irregular topology may be more costly and require more expertise than a vendor-specified DCN architecture. In par- ticular, addressing is more difficult to configure an irregular topology, because we cannot encode topologic locality in the logical ID of a switch (typically a switch’s logical ID is its topology-imposed address or label). However, such a network can be configured using Chen et al.’s generic and automatic data center address configuration system (DAC) [9]. DAC automates the assignment of logical IDs (e.g., IP addresses or node labels) to network devices. DAC begins with a network blueprint which specifies the logical ID, and then automatically learns devices IDs (e.g., MAC addresses). An interesting benefit of DAC’s design is that it can automatically identify mis-wirings. This operation is especially useful for us because wiring an arbitrary topology may be more difficult than a regular, tree-like topology. We believe DAC can solve many of the management problems that may arise from the introduction of irregular topologies in the data center, and we leave further investigation to future work.
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A Buffering strategy for stabilizing network data rates

A Buffering strategy for stabilizing network data rates

job, and the and with the the client and server including both Under that plan application, that applies the method include implementations and to receive Each Daemon the daemon without [r]

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Social network analysis

Social network analysis

informational advantages, is network centrality of these firms related to financial performance? Traditional statistical techniques used in the social sciences (see Chapter 10 in this Handbook) cannot be used to answer such questions because the distributions of network connections are highly variable, highly sensitive to small changes in parameters, and thus unstandardized, to date. So the standard statistical technique of comparing observed data to standardized distributions (normal, Poisson, and so on) cannot be used to identify atypical phenomena. Secondly, standard regression techniques assume independence of variables, whereas network data is inherently interdependent: connections among nodes are likely to be maintained and reinforced over time through network effects such as popularity and reciprocity. Attempts to employ standard statistical techniques to network data are prone to the identification of spurious associations, generally overstating the effects of connectivity (Borgatti et al. 2013; Snijders et al. 2010).
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A Review Paper on Social Network Analysis through Data Mining Techniques

A Review Paper on Social Network Analysis through Data Mining Techniques

Abstract-- Data Mining is that the extraction of projected information from large data sets cab be a pleasant innovative technology. The goal of the data mining method is to extract information from an information set and work on it into an obvious structure for additional use. Websites contain several unprocessed information. By analyzing this information new knowledge is gained.

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Data Leak Protection Using Text Mining and Social Network Analysis

Data Leak Protection Using Text Mining and Social Network Analysis

Social network analysis involves the mapping and measuringof relationships between people, groups and organizationsby representing the relationships in terms of nodes and connections.Social networks can be derived from communicationchannels such as email, forum discussions, and social networkingsites. Analysis of social networks can improve ourunderstanding of the relationships and groupings between theparties involved in electronic communications, email in particular.Thus the goal of social network analysis for data leakprevention is to identify the communication patterns withinthe organization and employ feedback from the administratorto identify unusual communications to uncover data leaks.Diesner et al. performed a social network analysis ofthe Enron email, which contains the email communicationsof top-level Enron employees before and during theEnron scandal. Applying social network analysis in data leak preventioninvolves monitoring the online collaborations (email, documentand code repositories) to discover the communities ofcollaboration. The discovered communities (i.e. social networks)are vital in identifying the collaborating parties suchas a team of developers working on the same code repositoryor a group of employees exchanging emails to perform atask (e.g. preparing for a meeting). Social network analysishas the potential to discover the collaborations which are notdocumented as a part of company policy or access control.Proper visualization of the communities can be presented tothe administrator for manual or automatic validation. Duringdeployment, if a substantial change in the social network isobserved, it is flagged for further analysis since it can reveal:(1) a dissolving social network (2) a merging social networkor (3) inclusion of an untrusted party, which is potentially adata leak.
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