Earticle

Cloud computing is an emerging web-based commercial computing paradigm in which the dynamic optimization allocation of resources is the core issue. This paper proposes a market mechanism model of resources binding service dynamically. First, the resources usability evaluation strategy based on D-S theory is given. Then we present fair value based resource pricing strategy and resource dynamic optimization allocation algorithm to achieve efficient allocation of system resources and long-run equilibrium. Simulation results show that the proposed model and strategy are effective in managing the cloud computing resources.

Foraging information from web forums is still one of the most challenging information retrieval tasks due to various combinations of auto-generated page structural information and user-created contents. Traditional information extraction methods employ either duplicated subtree pattern detection methods, or machine learning methods. Due to the periodical update of forum templates and diversity of page contents, aforementioned approaches do not work very well on forum sites. In this paper, we present a page-segmentation based wrapper specially designed for mining data pattern of web forums, which combines a novel page segmentation algorithm and decision tree classifier together to detect the data pattern in forum. In the segmentation phase, a novel page segmentation algorithm is proposed to identify the records areas in a page, then a classifier is adopted to identify the detailed pattern of each record in the extraction phase. Extensive experiments on various types of web forums are conducted and the results conclude that our wrapper is a more generalized one which requires only few labeled training data.

The rapidly improving technologies like data mining and mobile technology need careful investigation in order to emerge these technologies. In this paper we identified the challenges confront by mobile data mining, visualization challenges, and mobile device limitations. The paper introduced a comprehensive framework that explores the idea, how we can incorporate both of these promising technologies regarding the challenges facing mobile data mining and all possible application scenarios. The paper further specifies the possible ways to merge both of these technologies, with the available resources.

With the dramatic increase of data volume, automatic data distribution has been one of the key techniques and intractable problem for distributed systems. This work summarizes the problem of data distribution and abstracts it as a general triangle model called DaWN. Based on data and workload analysis, it presents a novel strategy called ADDS for automatic data distribution in OLTP applications. According to the results of a series of experiments over TPC-C datasets and transactions, the proposed approach shows effective improvements.

Increasing access to large-scale, high-dimensional and non-stationary streams in many real applications has made it necessary to design new dynamic classification algorithms. Most existing approaches for the textual stream classification are able to train the model relying on labeled data. However, only a limited number of instances can be labeled in a real streaming environment since large-scale data appear at a high speed. Therefore, it is useful to make unlabeled instances available for training and updating the ensemble models. In this paper, we present a new ensemble framework with partial labeled instances for learning from the textual stream. A new semi-supervised cluster-based classifier is proposed as the sub-classifier in our approach. In order to integrate these sub-classifiers, we propose an adaptive selection method. Empirical evaluation of textual streams reveals that our approach outperforms state-of-the-art stream classification algorithms.

Block level storage system has been widely used as it can provide users raw block device. On the basis of Orthrus system, this paper designs and implements a multiple volume servers block level storage system based on load-balanced strategy-Orthrus Plus. This system uses the dynamic load-balanced strategy based on genetic algorithm strategy to load balance the volume servers clusters and proposes an iSCSI pause and resume method to solve the breakpoint retransmission problem during load-balancing. At last, we design a hierarchical management mechanism based on volume life cycle at storage. Experiments show that the load balancing capability in Orthrus Plus is more accurate and efficient under the situation of larger amount of data, and the hierarchical storage strategy also increases the I/O throughout capacity.

Image retrieval is an emerging research area for multimedia database. It is the basic requirement task in the present scenario. In this paper we propose a novel CBIR approach using Fuzzy clustering. The proposed method firstly clusters the images based on RGB components, using Fuzzy clustering algorithm, secondly the image retrieval is performed based on texture element in the clustered database. The performance of the system is evaluated on the most common evaluation method namely Precision and Recall method. The proposed method achieves higher efficiency than the texture based retrieval techniques.

To accomplish face recognition more efficiently, a distributed face recognition framework based on MB-LBP features and data fusion is presented in this paper. Firstly, four face regions are interactively marked and the Multi-scale Block Local Binary Patterns are extracted from these regions to achieve both locally and globally informative features. Secondly, a distributed framework is introduced to accelerate the recognition process, in which features of each single face region are utilized to perform face classification in parallel. The final decision is made by a kind of data fusion mechanism based on an artificial neural network (ANN) to make rational use of the confidence information got from the classification of each region. In experiment, the runtime and recognition performance of our system is compared with several other popular face recognition paradigms. The results indicate that the distributed framework presented in this paper can promote the efficiency of face recognition prominently while not losing accuracy in recognition performance.

Knowledge Discovery in Databases (KDD) covers various processes of exploring useful information from voluminous data. These data may contain several inconsistencies, missing records or irrelevant features, which make the knowledge extraction, a difficult process. So, it is essential to apply pre-processing techniques to these data in order to enhance its quality. Detailed description of data cleaning, imbalanced data handling and dimensionality reduction pre-processing techniques are depicted in this paper. Another important aspect of Knowledge Discovery is to filter, integrate, visualize and evaluate the extracted knowledge. In this paper, several visualization techniques such as scatter plots, parallel co-ordinates and pixel oriented technique are explained. The paper also includes detail descriptions of three visualization tools which are DBMiner, Spotfire and WinViz along with their comparative evaluation on the basis of certain criteria. It also highlights the research opportunities and challenges of Knowledge Discovery process.

Data-intensive applications, such as data mining and data visualization, need to deal with huge amount of data. Service-oriented architecture, on the other hand, offers a scalable and flexible framework to implement loosely-coupled and standards-based distribute computing which the data-intensive applications usually require. In this paper, we apply four kinds of data-flow constructs, namely Map Construct, Reduce Construct, Conditional Construct and Loop Construct, to assist Web services composition to process huge volume of data. Furthermore, we put forward the approach to the composition of Web services based on the I/O operation ratio, in which the CPU intensive Web services and the I/O intensive ones are arranged to execute in parallel as far as possible. A case is presented finally to show that our approaches are feasible and effective.

Existing identity providing models are generally user-oriented or service provider-oriented, which can hardly meet the requirements of integration for heterogeneous resources of enterprise. This paper presents a unified identity providing model for accessing to heterogeneous resources: providing resource identity, identity mapping from heterogeneous resources identity to unified identity, and also providing identity providing interface for application software developing. Unified identity shields the differences of identity information among resources in heterogeneous environment, which makes users get the ability to access resources with one unified identity.

Aimed at resolving the second software crisis due to increasingly technical complexities and diversities of software development methods, Model Driven Architecture (MDA) was proposed in the late 2000. However, because of its lacking an effective and well-known modeling process or method, it is difficult to apply and promote MDA in the industry. In this paper, MDA is extended with a business model, and corresponding theory and presentation of the business model are explored, especially the dependencies and transformation mechanism of its sub-models are discussed. And a case of shopping is shown. Based on the business model, CIM is traced. And its advantages are discussed by compared with the Rational Unified Process (RUP) and Dines’ triptych paradigm. This research is helpful to apply MDA in the industry.

Security implementation in hierarchical SOAP based web services is essential from the perspectives of efficiency and reliability of the service. We present here a study on the use of encryption, signature and signature followed by encryption security standards in a hierarchical web service. The web service with the security policies is evaluated through the development of the application, testing of the service and evaluation of the performance metrics. The performance latencies are observed and compared to study the response of hierarchical web service against each security policies. Here we present in details the architecture of the service, software aspects, its testing procedure and the performance aspects of implemented security policy along with the outcomes of statistical analysis based on the observed performance metrics.

In order to improve the disadvantage in the process of constructing concept lattice which products enormous meaningless association rule, based on the analysis of the existing concept lattice and its improved model, attribute-oriented generalization is introduced into the research of concept lattice .A new lattice is proposed, which is called Generalized Attributive Concept Lattice. At the same time, qualities of Generalized Attributive concept lattice are proved. The constructing theory of Generalized Attributive concept lattice is analyzed. A novel algorithm of constructing concept lattice is proposed.

Data analytics and machine learning has always been of great importance in almost every field especially in business decision making and strategy building, in healthcare domain, in text mining and pattern identification on the web, in meteorological department, etc. The daily exponential growth of data today has shifted the normal data analytics to new paradigm of Big Data Analytics and Big Data Machine Learning. We need tools to perform online data analysis on streaming data for achieving faster learning and faster response in data analytics as well as maintaining scalability in terms of huge volume of data. SAMOA (Scalable Advanced Massive Online Analysis) is a recent framework in this reference. This paper discusses the architecture of this SAMOA framework and its directory structure. Also it expresses a practical experience of configuring and deployment of the tool for handling massive online analysis on Big Data.

This paper presents a self-organized fuzzy neural network (SOFNN) surface reconstruction algorithm suitable for point clouds without normal. It overcomes the defect of traditional Delaunay triangulation which is difficult to reconstruct point clouds with noises and implicit function which is limited to the number of point clouds and point clouds are required very strict. The SOFNN is based on the fuzzy clustering method optimizing training data before learning fuzzy rules, in order to remove noise data and resolve conflicts in data. The approach not only reduce computational burden of neural network, but also make it easy to fit the surface for point clouds without normal and suitable for mass point clouds. The feature of the SOFNN has dynamic self-organized structure, fast learning speed and flexibility in learning. The experiment results show that is very fine.

An electronic book is shortly denoted as e-book that consists of file format for personal computers, hardware devices, portable devices and Operating systems. It represents the ideal print book publication structure, classical content formatting, and its metaphor in digital editions. It includes text, image, sound and motion picture as main integral content. Academic print publications are massively available in various digital versions on internet at present. Still, there are numerously required for transferring academic course book, prescribed textbooks, student notes in open, extensible and personalization forms. This paper develops the concrete ideas about e-book in terms of open e-book publication which are consisting the XML-based standards for extensibility and personalization. These features are reported with the release of standards into academic course book publishing. Statistical analysis indicated that the students are being helped by implementing open e-book structure into their curriculum design. Furthermore, it is providing the students to create their personal learning environment on intranet and internet.

References can directly reflect quality of Undergraduate theses. However, it is a thorny problem that how to quantitatively evaluate massive undergraduate thesis references. A quality evaluation method of undergraduate thesis references based on Hadoop is presented which applies pattern matching algorithm to extract metadata and evaluates quality of undergraduate theses by quality evaluation model based on grey relational analysis. Experiment results show that this method can quickly and accurately evaluate quality of undergraduate thesis references on Hadoop.

Content syndication is the process of pushing the information out into third-party information providers. The idea is to drive more engagement with your content by wiring it into related digital contexts. However, there are some shortages of current related products, such as search challenges on massive feeds, synchronization performance, and user experience. To address these limitations, we aim to propose an improved architecture of content syndication and recommendation. First, we design a source listener to extract feed changes from different RSS sources, and propagate the incremental changes to target schema-free document stores to improve the search performance. Second, the proposed recommendation algorithm is to tidy, filter, and sort all the feeds before pushing them to the users automatically. Third, we provide some OAuth2-authorization RESTful feed sharing APIs for the integration with the third-party systems. The experimental result shows that this architecture speeds up the search and synchronization process, and provides friendlier user experience.

Nowadays, digital documents are widely used in many business areas since computer networks and digital devices such as smartphones have been evolved. However, digital documents may have very important information such as confidential business policies and intellectual property statements. Therefore, securing them is critical to the business world of today. In this paper we propose new enhanced security architecture for digital documents. The proposed architecture enhances the security of digital document using RFID with encryption algorithms. In other words, it provides more enhanced security not only using software but also hardware The proposed architecture could be used in many business areas which use high security digital documents, such as a secure intranet of organization. In addition to, our approach is able to use in digital rights management.

Log data is a valuable resource for failure prediction and troubleshooting in large-scale systems. However, with the rapid growth of the system scale and the popularity of various applications in productional environments, the volume of logs emerged per day becomes huge, posing serious challenges for storage and analysis. To solve these problems, we propose an online log filtering mechanism to eliminate the redundant and noisy log records through event filtering and instance filtering, aiming to minimize the log size without losing important information required for the fault diagnosis. Our proposed log filtering is evaluated on a real log data derived from a productional cloud computing system, observing that over 76% of the storage space are saved without losing important information.