Search results for: multimedia data.

Authors: N. P. Singh, Brahmjit Singh

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CDMA cellular networks support soft handover, which guarantees the continuity of wireless services and enhanced communication quality. Cellular networks support multimedia services under varied propagation environmental conditions. In this paper, we have shown the effect of characteristic parameters of the cellular environments on the soft handover performance. We consider path loss exponent, standard deviation of shadow fading and correlation coefficient of shadow fading as the characteristic parameters of the radio propagation environment. A very useful statistical measure for characterizing the performance of mobile radio system is the probability of outage. It is shown through numerical results that above parameters have decisive effect on the probability of outage and hence the overall performance of the soft handover algorithm.

Keywords: CDMA, Correlation coefficient, Path loss exponent, Probability of outage, Soft handover.

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: Data Estimation, link data, machine learning, road network.

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Authors: Youkyung Park, SeungYup Kang, SungHo Kim, SimKyun Yook

Abstract:

IMCS is Integrated Monitoring and Control System for thermal power plant. This system consists of mainly two parts; controllers and OIS (Operator Interface System). These two parts are connected by Ethernet-based communication. The controller side of communication is managed by CNet module and OIS side is managed by data server of OIS. CNet module sends the data of controller to data server and receives commend data from data server. To minimizes or balance the load of data server, this module buffers data created by controller at every cycle and send buffered data to data server on request of data server. For multiple data server, this module manages the connection line with each data server and response for each request from multiple data server. CNet module is included in each controller of redundant system. When controller fail-over happens on redundant system, this module can provide data of controller to data sever without loss. This paper presents three main features – separation of get task, usage of ring buffer and monitoring communication status –of CNet module to carry out these functions.

Keywords: Ethernet communication, DCS, power plant, ring buffer, data integrity

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Authors: Sanaa Chafik, ImaneDaoudi, Mounim A. El Yacoubi, Hamid El Ouardi

Abstract:

Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. In this paper, we propose a new hashing algorithm to overcome the storage space problem and improve query time, while keeping a good accuracy as similar to that achieved by the original Euclidean LSH. The Experimental results on a real large-scale dataset show that the proposed approach achieves good performances and consumes less memory than the Euclidean LSH.

Keywords: Approximate Nearest Neighbor Search, Content based image retrieval (CBIR), Curse of dimensionality, Locality sensitive hashing, Multidimensional indexing, Scalability.

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

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Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: Big data, big data Analytics, Hadoop framework, cloud computing.

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Authors: Tomy Prihananto, Damar Apri Sudarmadi

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Indonesia recognizes the right to privacy as a human right. Indonesia provides legal protection against data management activities because the protection of personal data is a part of human rights. This paper aims to describe the arrangement of data management and data management in Indonesia. This paper is a descriptive research with qualitative approach and collecting data from literature study. Results of this paper are comprehensive arrangement of data that have been set up as a technical requirement of data protection by encryption methods. Arrangements on encryption and protection of personal data are mutually reinforcing arrangements in the protection of personal data. Indonesia has two important and immediately enacted laws that provide protection for the privacy of information that is part of human rights.

Keywords: Indonesia, protection, personal data, privacy, human rights, encryption.

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Authors: Scott Dyer, Nicoletta Adamo-Villani

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In this paper we report a study aimed at determining the effects of animation on usability and appeal of educational software user interfaces. Specifically, the study compares 3 interfaces developed for the Mathsigner™ program: a static interface, an interface with highlighting/sound feedback, and an interface that incorporates five Disney animation principles. The main objectives of the comparative study were to: (1) determine which interface is the most effective for the target users of Mathsigner™ (e.g., children ages 5-11), and (2) identify any Gender and Age differences in using the three interfaces. To accomplish these goals we have designed an experiment consisting of a cognitive walkthrough and a survey with rating questions. Sixteen children ages 7-11 participated in the study, ten males and six females. Results showed no significant interface effect on user task performance (e.g., task completion time and number of errors); however, interface differences were seen in rating of appeal, with the animated interface rated more 'likeable' than the other two. Task performance and rating of appeal were not affected significantly by Gender or Age of the subjects.

Keywords: Animation, Animated interfaces, EducationalSoftware, Human Computer Interaction, Multimedia.

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Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: Data integration, data warehousing, federated architecture, online analytical processing.

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Authors: Martin Lnenicka

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Governments collect and produce large amounts of data. Increasingly, governments worldwide have started to implement open data initiatives and also launch open data portals to enable the release of these data in open and reusable formats. Therefore, a large number of open data repositories, catalogues and portals have been emerging in the world. The greater availability of interoperable and linkable open government data catalyzes secondary use of such data, so they can be used for building useful applications which leverage their value, allow insight, provide access to government services, and support transparency. The efficient development of successful open data portals makes it necessary to evaluate them systematic, in order to understand them better and assess the various types of value they generate, and identify the required improvements for increasing this value. Thus, the attention of this paper is directed particularly to the field of open data portals. The main aim of this paper is to compare the selected open data portals on the national level using content analysis and propose a new evaluation framework, which further improves the quality of these portals. It also establishes a set of considerations for involving businesses and citizens to create eservices and applications that leverage on the datasets available from these portals.

Keywords: Big data, content analysis, criteria comparison, data quality, open data, open data portals, public sector.

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Authors: C. Mongkol

Abstract:

The objective of this project is to produce computer assisted instruction(CAI) for welding and brazing in order to determine the efficiency of the instruction package and the study accomplishment of learner by studying through computer assisted instruction for welding and brazing it was examined through the target group surveyed from the 30 students studying in the two year of 5-year-academic program, department of production technology education, faculty of industrial education and technology, king mongkut-s university of technology thonburi. The result of the research indicated that the media evaluated by experts and subject matter quality evaluation of computer assisted instruction for welding and brazing was in line for the good criterion. The mean of score evaluated before the study, during the study and after the study was 34.58, 83.33 and 83.43, respectively. The efficiency of the lesson was 83.33/83.43 which was higher than the expected value, 80/80. The study accomplishment of the learner, who utilizes computer assisted instruction for welding and brazing as a media, was higher and equal to the significance statistical level of 95%. The value was 1.669 which was equal to 35.36>1.669. It could be summarized that computer assisted instruction for welding and brazing was the efficient media to use for studying and teaching.

Keywords: Computer Assisted Instruction, Achievement, Efficiency of the lesson, Evaluation

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Authors: S. Madhavi, S. Abirami, C. Bharathi, B. Ekambaram, T. Krishna Sankar, A. Nattudurai, N. Vijayarangan

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The high utilization rate of Automated Teller Machine (ATM) has inevitably caused the phenomena of waiting for a long time in the queue. This in turn has increased the out of stock situations. The ATM utilization helps to determine the usage level and states the necessity of the ATM based on the utilization of the ATM system. The time in which the ATM used more frequently (peak time) and based on the predicted solution the necessary actions are taken by the bank management. The analysis can be done by using the concept of Data Mining and the major part are analyzed based on the predictive data mining. The results are predicted from the historical data (past data) and track the relevant solution which is required. Weka tool is used for the analysis of data based on predictive data mining.

Keywords: ATM, Bank Management, Data Mining, Historical data, Predictive Data Mining, Weka tool.

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Authors: Jaechun No

Abstract:

As data to be stored in storage subsystems tremendously increases, data protection techniques have become more important than ever, to provide data availability and reliability. In this paper, we present the file system-based data protection (WOWSnap) that has been implemented using WORM (Write-Once-Read-Many) scheme. In the WOWSnap, once WORM files have been created, only the privileged read requests to them are allowed to protect data against any intentional/accidental intrusions. Furthermore, all WORM files are related to their protection cycle that is a time period during which WORM files should securely be protected. Once their protection cycle is expired, the WORM files are automatically moved to the general-purpose data section without any user interference. This prevents the WORM data section from being consumed by unnecessary files. We evaluated the performance of WOWSnap on Linux cluster.

Keywords: Data protection, Protection cycle, WORM

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Authors: Aldalalah, M. Osamah, Soon Fook Fong

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The purpose of this study was to investigate the effects of computer–based instructional designs, namely modality and redundancy principles on the attitude and learning of music theory among primary pupils of different Music Intelligence levels. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The independent variables were the three modes of courseware. The moderator variable was music intelligence. The dependent variables were the post test score. ANOVA was used to determine the significant differences of the pretest scores among the three groups. Analyses of covariance (ANCOVA) and Post hoc were carried out to examine the main effects as well as the interaction effects of the independent variables on the dependent variables. High music intelligence pupils performed significantly better than low music intelligence pupils in all the three treatment modes. The AI mode was found to help pupils with low music intelligence significantly more than the TI and AIT modes.

Keywords: Modality, Redundancy, Music theory, Cognitivetheory of multimedia learning, Cognitive load theory, Musicintelligence.

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Authors: Pavel Vazan, Pavol Tanuska, Michal Kebisek

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The paper gives the pilot results of the project that is oriented on the use of data mining techniques and knowledge discoveries from production systems through them. They have been used in the management of these systems. The simulation models of manufacturing systems have been developed to obtain the necessary data about production. The authors have developed the way of storing data obtained from the simulation models in the data warehouse. Data mining model has been created by using specific methods and selected techniques for defined problems of production system management. The new knowledge has been applied to production management system. Gained knowledge has been tested on simulation models of the production system. An important benefit of the project has been proposal of the new methodology. This methodology is focused on data mining from the databases that store operational data about the production process.

Keywords: data mining, data warehousing, management of production system, simulation

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila

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There have been a lot of efforts and researches undertaken in developing efficient tools for performing several tasks in data mining. Due to the massive amount of information embedded in huge data warehouses maintained in several domains, the extraction of meaningful pattern is no longer feasible. This issue turns to be more obligatory for developing several tools in data mining. Furthermore the major aspire of data mining software is to build a resourceful predictive or descriptive model for handling large amount of information more efficiently and user friendly. Data mining mainly contracts with excessive collection of data that inflicts huge rigorous computational constraints. These out coming challenges lead to the emergence of powerful data mining technologies. In this survey a diverse collection of data mining tools are exemplified and also contrasted with the salient features and performance behavior of each tool.

Keywords: Business Analytics, Data Mining, Data Analysis, Machine Learning, Text Mining, Predictive Analytics, Visualization.

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Authors: Dennis A. Apuan

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Categorical data based on description of the agricultural landscape imposed some mathematical and analytical limitations. This problem however can be overcome by data transformation through coding scheme and the use of non-parametric multivariate approach. The present study describes data transformation from qualitative to numerical descriptors. In a collection of 103 random soil samples over a 60 hectare field, categorical data were obtained from the following variables: levels of nitrogen, phosphorus, potassium, pH, hue, chroma, value and data on topography, vegetation type, and the presence of rocks. Categorical data were coded, and Spearman-s rho correlation was then calculated using PAST software ver. 1.78 in which Principal Component Analysis was based. Results revealed successful data transformation, generating 1030 quantitative descriptors. Visualization based on the new set of descriptors showed clear differences among sites, and amount of variation was successfully measured. Possible applications of data transformation are discussed.

Keywords: data transformation, numerical descriptors, principalcomponent analysis

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Authors: J. X. Dong, C. J. An, Z. Chen, E. H. Owens, M. C. Boufadel, E. Taylor, K. Lee

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Shorelines are vulnerable to significant environmental impacts from oil spills. Stranded oil can cause potential short- to long-term detrimental effects along beaches that include injuries to ecosystem, socio-economic and cultural resources. In this study, a three-dimensional (3D) numerical modeling approach is developed to evaluate the fate and transport of spilled oil for hypothetical oiled shoreline cases under various combinations of beach geomorphology and environmental conditions. The developed model estimates the spatial and temporal distribution of spilled oil for the various test conditions, using the finite volume method and considering the physical transport (dispersion and advection), sinks, and sorption processes. The model includes a user-friendly interface for data input on variables such as beach properties, environmental conditions, and physical-chemical properties of spilled oil. An experimental meso-scale tank design was used to test the developed model for dissolved petroleum hydrocarbon within shorelines. The simulated results for effects of different sediment substrates, oil types, and shoreline features for the transport of spilled oil are comparable to that obtained with a commercially available model. Results show that the properties of substrates and the oil removal by shoreline effects have significant impacts on oil transport in the beach area. Sensitivity analysis, through the application of the one-step-at-a-time method (OAT), for the 3D model identified hydraulic conductivity as the most sensitive parameter. The 3D numerical model allows users to examine the behavior of oil on and within beaches, assess potential environmental impacts, and provide technical support for decisions related to shoreline clean-up operations.

Keywords: dissolved petroleum hydrocarbons, environmental multimedia model, finite volume method, FVM, sensitivity analysis, total petroleum hydrocarbons, TPH

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Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: Semantic data integration, biological ontology, linked data, semantic web, OWL, RDF.

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Authors: Abdessamad Essaouabi, Ibnelhaj Elhassane

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Efficient storage, transmission and use of video information are key requirements in many multimedia applications currently being addressed by MPEG-4. To fulfill these requirements, a new approach for representing video information which relies on an object-based representation, has been adopted. Therefore, objectbased watermarking schemes are needed for copyright protection. This paper proposes a novel blind object watermarking scheme for images and video using the in place lifting shape adaptive-discrete wavelet transform (SA-DWT). In order to make the watermark robust and transparent, the watermark is embedded in the average of wavelet blocks using the visual model based on the human visual system. Wavelet coefficients n least significant bits (LSBs) are adjusted in concert with the average. Simulation results shows that the proposed watermarking scheme is perceptually invisible and robust against many attacks such as lossy image/video compression (e.g. JPEG, JPEG2000 and MPEG-4), scaling, adding noise, filtering, etc.

Keywords: Watermark, visual model, robustness, in place lifting shape adaptive-discrete wavelet transform.

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Authors: Satoshi Komorita, Tsunehiko Chiba, Hidetoshi Yokota, Ashutosh Dutta, Christian Makaya, Subir Das, Dana Chee, F. Joe Lin, Henning Schulzrinne

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The NGN (Next Generation Network), which can provide advanced multimedia services over an all-IP based network, has been the subject of much attention for years. While there have been tremendous efforts to develop its architecture and protocols, especially for IMS, which is a key technology of the NGN, it is far from being widely deployed. However, efforts to create an advanced signaling infrastructure realizing many requirements have resulted in a large number of functional components and interactions between those components. Thus, the carriers are trying to explore effective ways to deploy IMS while offering value-added services. As one such approach, we have proposed a self-organizing IMS. A self-organizing IMS enables IMS functional components and corresponding physical nodes to adapt dynamically and automatically based on situation such as network load and available system resources while continuing IMS operation. To realize this, service continuity for users is an important requirement when a reconfiguration occurs during operation. In this paper, we propose a mechanism that will provide service continuity to users and focus on the implementation and describe performance evaluation in terms of number of control signaling and processing time during reconfiguration

Keywords: IMS, SIP, Service Continuity, Self-organizing, and Performance.

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: Clustering algorithms, coastal engineering, data mining, data summarization, statistical methods.

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Authors: Manas Ranjan Kabat, Manoj Kumar Patel, Chita Ranjan Tripathy

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Many multimedia communication applications require a source to transmit messages to multiple destinations subject to quality of service (QoS) delay constraint. To support delay constrained multicast communications, computer networks need to guarantee an upper bound end-to-end delay from the source node to each of the destination nodes. This is known as multicast delay problem. On the other hand, if the same message fails to arrive at each destination node at the same time, there may arise inconsistency and unfairness problem among users. This is related to multicast delayvariation problem. The problem to find a minimum cost multicast tree with delay and delay-variation constraints has been proven to be NP-Complete. In this paper, we propose an efficient heuristic algorithm, namely, Economic Delay and Delay-Variation Bounded Multicast (EDVBM) algorithm, based on a novel heuristic function, to construct an economic delay and delay-variation bounded multicast tree. A noteworthy feature of this algorithm is that it has very high probability of finding the optimal solution in polynomial time with low computational complexity.

Keywords: EDVBM, Heuristic algorithm, Multicast tree, QoS routing, Shortest path.

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Authors: Charles D. Otine, Samuel B. Kucel, Lena Trojer

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Selecting the data modeling technique for an information system is determined by the objective of the resultant data model. Dimensional modeling is the preferred modeling technique for data destined for data warehouses and data mining, presenting data models that ease analysis and queries which are in contrast with entity relationship modeling. The establishment of data warehouses as components of information system landscapes in many organizations has subsequently led to the development of dimensional modeling. This has been significantly more developed and reported for the commercial database management systems as compared to the open sources thereby making it less affordable for those in resource constrained settings. This paper presents dimensional modeling of HIV patient information using open source modeling tools. It aims to take advantage of the fact that the most affected regions by the HIV virus are also heavily resource constrained (sub-Saharan Africa) whereas having large quantities of HIV data. Two HIV data source systems were studied to identify appropriate dimensions and facts these were then modeled using two open source dimensional modeling tools. Use of open source would reduce the software costs for dimensional modeling and in turn make data warehousing and data mining more feasible even for those in resource constrained settings but with data available.

Keywords: About Database, Data Mining, Data warehouse, Dimensional Modeling, Open Source.

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Authors: Tong Liu, Xuan Xu, Xiaodi Wang

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Computational techniques derived from digital image processing are playing a significant role in the security and digital copyrights of multimedia and visual arts. This technology has the effect within the domain of computers. This research presents discrete M-band wavelet transform (MWT) and cosine transform (DCT) based watermarking algorithm by incorporating the principal component analysis (PCA). The proposed algorithm is expected to achieve higher perceptual transparency. Specifically, the developed watermarking scheme can successfully resist common signal processing, such as geometric distortions, and Gaussian noise. In addition, the proposed algorithm can be parameterized, thus resulting in more security. To meet these requirements, the image is transformed by a combination of MWT & DCT. In order to improve the security further, we randomize the watermark image to create three code books. During the watermark embedding, PCA is applied to the coefficients in approximation sub-band. Finally, first few component bands represent an excellent domain for inserting the watermark.

Keywords: discrete M-band wavelet transform , discrete M-band wavelet transform, randomized watermark, principal component analysis

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Authors: Wei-hua Ai, Wei Yan, Xiang Li

Abstract:

Data compression is used operationally to reduce bandwidth and storage requirements. An efficient method for achieving lossless weather radar data compression is presented. The characteristics of the data are taken into account and the optical linear prediction is used for the PPI images in the weather radar data in the proposed method. The next PPI image is identical to the current one and a dramatic reduction in source entropy is achieved by using the prediction algorithm. Some lossless compression methods are used to compress the predicted data. Experimental results show that for the weather radar data, the method proposed in this paper outperforms the other methods.

Keywords: Lossless compression, weather radar data, optical linear prediction, PPI image

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

Abstract:

The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: Data management, digitization, Industry 4.0, knowledge engineering, metamodel.

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Authors: Bogdan Walek, Cyril Klimes

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In present days the area of data migration is very topical. Current tools for data migration in the area of relational database have several disadvantages that are presented in this paper. We propose a methodology for data migration of the database tables and their data between various types of relational database systems (RDBMS). The proposed methodology contains an expert system. The expert system contains a knowledge base that is composed of IFTHEN rules and based on the input data suggests appropriate data types of columns of database tables. The proposed tool, which contains an expert system, also includes the possibility of optimizing the data types in the target RDBMS database tables based on processed data of the source RDBMS database tables. The proposed expert system is shown on data migration of selected database of the source RDBMS to the target RDBMS.

Keywords: Expert system, fuzzy, data migration, database, relational database, data type, relational database management system.

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Authors: Jixin Liu, Zheming Lu

Abstract:

In this paper, a novel multipurpose audio watermarking algorithm is proposed based on Vector Quantization (VQ) in Discrete Cosine Transform (DCT) domain using the codeword labeling and index-bit constrained method. By using this algorithm, it can fulfill the requirements of both the copyright protection and content integrity authentication at the same time for the multimedia artworks. The robust watermark is embedded in the middle frequency coefficients of the DCT transform during the labeled codeword vector quantization procedure. The fragile watermark is embedded into the indices of the high frequency coefficients of the DCT transform by using the constrained index vector quantization method for the purpose of integrity authentication of the original audio signals. Both the robust and the fragile watermarks can be extracted without the original audio signals, and the simulation results show that our algorithm is effective with regard to the transparency, robustness and the authentication requirements

Keywords: Copyright Protection, Discrete Cosine Transform, Integrity Authentication, Multipurpose Audio Watermarking, Vector Quantization.

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Authors: K. Hardy, A. Maurushat

Abstract:

Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.

Keywords: Big data, open data, productivity, transparency.

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Authors: Yu-Mi Song, Sung-Ah Kim, Dongyoun Shin

Abstract:

Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.

Keywords: Big data, correlation analysis, data recommendation system, urban data network.

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