Journal articles on the topic 'Multimedia Database; Data Mining; Multimedia Information Retrieval'

Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users’ query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches.

The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

In the past decades, we have witnessed an explosion of multimedia data, especially with the development of social media websites and blooming popularity of smart devices. As a result, multimedia semantic concept mining and retrieval whose objective is to mine useful information from the large amount of multimedia data including texts, images, and videos has become more and more important. The huge amount of multimedia data and the semantic gap between low-level features and high-level semantic concepts have made it even more challenging. To address these challenges, the correlations among the classes can provide important context cues to help bridge the semantic gap. Meanwhile, many real-world datasets do not have uniform class distributions while the minority instances actually represent the concept of interests, like frauds in transactions, intrusions in network security, and unusual events in surveillance. Despite extensive research efforts, imbalanced concept retrieval remains one of the most challenging research problems in multimedia data mining. Different from existing frameworks regarding concept correlations among labels, this paper presents a novel concept correlation analysis model using the correlation between the retrieval scores and labels. Experimental results on the TRECVID benchmark datasets demonstrate that the proposed framework can enhance imbalanced concept mining and retrieval even with trivial scores from the minority class.

The exponential growth of the technological advancements has resulted in high-resolution devices, such as digital cameras, scanners, monitors, and printers, which enable the capturing and displaying of multimedia data in high-density storage devices. Furthermore, more and more applications need to live with multimedia data. However, the gap between the characteristics of various media types and the application requirements has created the need to develop advanced techniques for multimedia data management and the extraction of relevant information from multimedia databases. Though many research efforts have been devoted to the areas of multimedia databases and data management, it is still far from maturity. The purpose of this article is to discuss how the existing techniques, methodologies, and tools addressed relevant issues and challenges to enable a better understanding in multimedia databases and data management. The focuses include: (1) how to develop a formal structure that can be used to capture the distinguishing content of the media data in a multimedia database (MMDB) and to form an abstract space for the data to be queried; (2) how to develop advanced content analysis and retrieval techniques that can be used to bridge the gaps between the semantic meaning and low-level media characteristics to improve multimedia information retrieval; and (3) how to develop query mechanisms that can handle complex spatial, temporal, and/or spatio-temporal relationships of multimedia data to answer the imprecise and incomplete queries issued to an MMDB.

With the continuous development of modern multimedia technology, the integration of computer technology into the teaching of various subjects has become a trend of the times. The application of computer media and network technology in mathematics teaching improves the integration of mathematics teaching and the integration of resources. A mathematics teaching network media fusion technology is proposed based on big data mining and information fusion, which combines the characteristics of multimedia and network technology in opening, creativity, subjectivity, and so on, and the database model of mathematics teaching is constructed. The multithread integrated scheduling method is used to design the mathematics teaching database model, the fuzzy control method is used to control the multimedia in mathematics teaching, and the big data association rule mining method is used to realize the information fusion of mathematics teaching resources. The optimization and integration of mathematics teaching resources and adaptive scheduling are realized under the technology of computer media and network, and the level of mathematics teaching is improved. The test results show that using this method to design the computer network media of mathematics teaching has a better ability of integrating and dispatching mathematics teaching resources, and the integration of mathematics teaching resources is stronger, which promotes the improvement of mathematics teaching level.

Classification, which is commonly used task in data mining applications separates the data present in the database based on some category. For years and years, considering the rise of several privacy issues, solutions in the form of theoretical and practical have been proposed for the classification problem under various security models. However, for the late Notoriety about cloud computing, clients presently have the chance on outsource their data, clinched alongside encrypted form, and also those information mining assignments of the cloud.. The data on the cloud which is in encrypted form, therefore existing privacy preserving classification techniques are not applicable. In this paper, we focus on finding solution for the classification problem over the encrypted data .Users can store their data with encryption by the use of ordered relational data. So, the data is obtained correctly without decrypting.

Educational objectives: To perform a collection of patient information, videolaryngoscopy, videolaryngostroboscopy, and acoustic data using a rigorous data collection scheme and to use a computerized interface for digitizing acoustic, video and patient data into a multimedia database for archiving and retrieval.

Summary Objectives: Self-directed and customized medical education programs are gaining importance in health care instruction. We prototypically implemented a repository-driven online computer system (CardioOP) for teleteaching in Heart Surgery. It supports authoring and multiple re-use of multimedia data for different user groups in different instructional applications and therefore requires a process of content management. Methods: We defined objectives for a terminological system to support semantic, cross-media type annotation and retrieval of learning objects: domain completeness, German (natural) language processing, multi-user concepts, extensibility and maintenance, content based annotation and technical implementation. Existing terminologies (ICD10, READ V3, Snomed III, UMLS 1997, MESH) have been analysed according to these objectives. Results: We found that the analysed terminologies did not meet our criteria sufficiently. Therefore, we developed a domain-specific thesaurus, the CardioOPDataClas (CDC). The application of the CDC within a database-driven authoring process using specifically developed tools is reported. Conclusions: Metadata play an important role in the effective discovery and search, access, integration and management of educational multimedia data in medicine but so far, there is no terminology to support content management for instructional multimedia. We prototypically designed and applied a thesaurus for the CardioOP educational system. Additional work is needed to evaluate the system in terms of user-friendliness, concept coverage and information retrieval performance.

With broadband networks, audio/video coding, and processing techniques and user devices swiftly advancing, multimedia streaming over networks is now a reality. Distributed interactive multimedia applications ? one of the fastest growing market sectors demanding innovations ? are covered in this special issue on multimedia processing (MP). This special issue focuses on ambitious and intriguing papers from experts on a wide variety of multimedia areas. These articles address innovations and offer effective solutions to MP problems. Y. Fushio et al. propose a shadow generation system based on shape recognition, while M. Suzuki et al. develop musical expression generation reflecting the userfs impression in kansei space and fuzzy rules. K. Ishizuka et al. evaluate an operetta songs generation system based on the impressions given by story scenes, which are strongly related to the integrative research projects of kansei engineering, music, text, and visual perception. T. Toyota et al. develop a web news visualization system based on an efficient self-organizing map using a restricted region search and dimensionality reduction. S. Kawauchi et al. propose a knowledge expansion support system using related search keyword generation based on Wikipedia categories and point-wise mutual information, involving that are aggressive looking progressive research projects covering huge text information. T. Tanabata et al. present an interactive data mining tool for microarray data analysis using formal concept analysis, which adds a new dimension to the bio-informatics field. T. Kawamura et al. analyze keywords input to a book search engine. T. Sugimoto et al. propose a recommendation system involving the use of comprehensive trend indications based on weighted complete graphs, which are related to topical discussions in the information retrieval and recommendation field. Readers are sure to find new and valuable information and ideas in their own fields in these excellent articles.

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.

Recently, advanced multimedia applications, such as geographic information system, and content-based multimedia retrieval system, require the efficient processing of k-nearest neighbor queries over large collection of multimedia objects. These queries usually include the semantic information that is represented by text, as well as the visual information that is represented by a high-dimensional feature vector. Among the available techniques for processing such queries, the incremental nearest neighbor algorithm proposed by Hjaltason and Samet is known as the best choice. However, the R-tree used in their algorithm has no facility capable of partially pruning the candidate tuples that will turn out not to satisfy the semantic predicate. Also, the R-tree does not perform sufficiently well on high-dimensional data even though it provides good results on low or middle-dimensional data. These drawbacks may lead to a poor performance when processing the query. In this paper, we propose an integrated index structure, so-called SPY-TEC+, that provides an efficient method for indexing the visual and semantic feature at the same time using the SPY-TEC that was proposed for indexing high-dimensional data, and the signature file. We also propose an efficient incremental nearest neighbor algorithm for processing k-nearest neighbor queries with visual and semantic predicates on the SPY-TEC+. Finally, we show that the SPY-TEC+ enhances the performance of the SPY-TEC for processing k-nearest neighbor queries with visual and semantic predicates through various experiments.

This paper describes various aspects of web information management with particular emphasis on its application to electronic commerce. We first provide a brief overview of the web. Then we discuss concepts for web database management, as database management is a key part of information management. These include data models and architectures, query processing, transaction management, metadata management, storage issues, and integrity and security. Then we discuss various web information management technologies such as multimedia, visualization, data mining and warehousing, and knowledge management. Then we discuss emerging standards such as Java Database Connectivity, Extended Markup Language (XML), and middleware standards such as Object Request Brokers (ORB) and Remote Method Invocation (RMI). Finally we discuss how web data management technologies can be applied to the important area of electronic commerce.

Quantization has been widely adopted for large-scale multimedia retrieval due to its effectiveness of coding highdimensional data. Deep quantization models have been demonstrated to achieve the state-of-the-art retrieval accuracy. However, training the deep models given a large-scale database is highly time-consuming as a large amount of parameters are involved. Existing deep quantization methods often sample only a subset from the database for training, which may end up with unsatisfactory retrieval performance as a large portion of label information is discarded. To alleviate this problem, we propose a novel model called Similarity Preserving Deep Asymmetric Quantization (SPDAQ) which can directly learn the compact binary codes and quantization codebooks for all the items in the database efficiently. To do that, SPDAQ makes use of an image subset as well as the label information of all the database items so the image subset items and the database items are mapped to two different but correlated distributions, where the label similarity can be well preserved. An efficient optimization algorithm is proposed for the learning. Extensive experiments conducted on four widely-used benchmark datasets demonstrate the superiority of our proposed SPDAQ model.

Purpose Data mining is the process of detecting knowledge from a given huge data set. Among the data set, multimedia is the data which contains diverse data such as audio, video, image, text and motion. In this growing field of video data, mining the video data plays vital role in the field of video data mining. In video data mining, video data are grouped into frames. In this vast amount of video frames, the fast retrieval of needed information is important one. This paper aims to propose a Birch-based clustering method for content-based image retrieval. Design/methodology/approach In image retrieval system, image segmentation plays a very important role. A text file, normally, is divided into sections, that is, piece, sentences, word and character for this information which are organized and indexed effectively like in a video, the information is dynamic in nature and this information is converted to static for easy retrieval. For this, video files are divided into a number of frames or segments. After the segmentation process, images are trained for retrieval process, and from these, unwanted images are removed from the data set. The noise or unwanted image removal pseudo-code is shown below. In the code image, pixel value represents the value of the difference between the two adjacent image pixel values. By assuming a threshold for the image value, the duplicate images are found. After finding the duplicate image, it is removed from the data set. Clustering is used in many applications as a stand-alone tool to get insight into data distribution and as a pre-processing step for other algorithms (Ester et al., 1996). Specifically, it is used in pattern recognition, spatial data analysis, image processing, economic science document classification, etc. Hierarchical clustering algorithms are classified as agglomerative or divisive. BRICH uses clustering attribute (CA) and clustering feature hierarchy (CA_Hierarchy) for the formation of clusters. It perform multidimensional data objects. Every BRICH algorithm based on the memory-oriented information, that is, memory constrains, is involved in the processing of the data sets. This information is represented in Figures 6-10. For forming clusters, they use the amount of object in the cluster (A), the sum of all points in the data set (S) and need the square value of the all objects (P). Findings The proposed technique brings an effective result for cluster formation. Originality/value BRICH uses a novel approach to model the degree of inter-connectivity and closeness between each pair of clusters that takes into account the internal characteristics of the clusters themselves.

This paper reports on the 28th Italian Symposium on Advanced Database Systems (SEBD 2020), held online as a virtual conference from the 21st to the 24th of June 2020. The topics that were addressed in this edition of the conference were organized in the sessions: ontologies and data integration, anomaly detection and dependencies, text analysis and search, deep learning, noSQL data, trajectories and diffusion, health and medicine, context and ranking, social and knowledge graphs, multimedia content analysis, security issues, and data mining.

<p><strong>Abstract.</strong> In this paper, we propose the design and implementation of the new geo-tagged media management system. A large amount of daily geo-tagged media data generated by user’s smart phone, mobile device, dash cam and camera. Geo-tagged media, such as geovideos and geophotos, can be captured with spatial temporal information such as time, location, visible area, camera direction, moving direction and visible distance information. Due to the increase in geo-tagged multimedia data, the researches for efficient managing and mining geo-tagged multimedia are newly expected to be a new area in database and data mining. This paper proposes a geotagged media management system, so called Open GeoCMS (Geo-tagged media Contents Management System). Open GeoCMS is a new framework to manage geo-tagged media data on the web. Our framework supports various types which are for moving point, moving photo – a sequence of photos by a drone, moving double and moving video. Also, GeoCMS has the label viewer and editor system for photos and videos. The Open GeoCMS have been developed as an open source system.</p>

This theme focuses on representing and reasoning high-level semantic based on concepts and their space relations. As to multimedia data, such as image and video, acquiring, representing and retrieving high-level semantic information has been a confused problem for a long time. Without the support of knowledge database, it is an impossible mission to carry out the simple synonymous retrieval, let alone retrieving the abstract semantic. This paper proposes some algorithms to translate restored concepts and their relations into a Concept Semantic Network, which is visualized by SVG finally. The paper also introduces the method of recording concepts distribution by description logic, which services users with concepts and distribution retrieval.

Semantic knowledge detection of multimedia content has become a very popular research topic in recent years. The association rule mining (ARM) technique has been shown to be an efficient and accurate approach for content-based multimedia retrieval and semantic concept detection in many applications. To further improve the performance of traditional association rule mining technique, a video semantic concept detection framework whose classifier is built upon a new weighted association rule mining (WARM) algorithm is proposed in this article. Our proposed WARM algorithm is able to capture the different significance degrees of the items (feature-value pairs) in generating the association rules for video semantic concept detection. Our proposed WARM-based framework first applies multiple correspondence analysis (MCA) to project the features and classes into a new principle component space and discover the correlation between feature-value pairs and classes. Next, it considers both correlation and percentage information as the measurement to weight the feature-value pairs and to generate the association rules. Finally, it performs classification by using these weighted association rules. To evaluate our WARM-based framework, we compare its performance of video semantic concept detection with several well-known classifiers using the benchmark data available from the 2007 and 2008 TRECVID projects. The results demonstrate that our WARM-based framework achieves promising performance and performs significantly better than those classifiers in the comparison.

Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%.

Retrieving of information from the huge set of data flowing due to the day to day development in the technologies has become more popular as it assists in searching for the valuable information in a structured, unstructured or a semi structured data set like text, database, multimedia, documents, and internet etc. The retrieval of information is performed employing any one of the models starting from the simple Boolean model for retrieving information, or using other frame works such as probabilistic, vector space and the natural language modelling. The paper is emphasis on using a natural language model based information retrieval to recover the meaning insights from the enormous amount of data. The method proposed in the paper uses the latent semantic analysis to retrieve significant information’s from the question raised by the user or the bulk documents. The carried out method utilizes the fundamentals of semantic factor occurring in the data set to identify the useful insights. The experiment analysis of the proposed method is carried out with few state of art dataset such as TIME, LISA, CACM and the NPL etc. and the results obtained demonstrate the superiority of the method proposed in terms of precision, recall and F-score.

Fuelled by the advancement in multimedia technologies, users across the world have witnessed the proliferation of online videos. Compared with the visual content of these videos, the textual content, for example, titles, tags, or descriptions, has been more broadly exploited in the real-world video data mining or information retrieval tasks. To enhance the understanding of videos, and improve the performance of the tasks such as automatic video annotation, video clustering, and cross-modal tag cleansing, the textual and visual content of videos are combined, through various methods. However, the absence of an empirical study on the properties of these contents makes them less solid to gain satisfactory performance. Therefore, in this paper, we conduct this study to verify the properties of textual content and draw insights from these analyses to promote further developments in video data mining that combine the two contents.

The Informa ionization of social life and the globalization of economy have made the importance of English increasingly prominent. Building an information-based teaching platform for supplementary teaching under the network environment has become a mainstream teaching method in various basic schools. How to integrate various types of multimedia teaching resources into English classroom teaching has become the main goal of the current college teaching reform. Aiming at the shortcomings of the current English education classroom, this paper designs and develops an English education cloud classroom based on the Internet of Things and data mining methods. First of all, the system adopts a three-tier B/S model, the development platform chooses, NET, the development language, uses ASP.NET, and the database chooses SQL server. Secondly, the data mining method is used to clean and organize the data in the cloud classroom background to explore the course education status behind the data. Finally, the simulation test analysis verifies the efficiency of the English education cloud classroom established in this article.

Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.

The research was conducted to develop a search system undergraduated thesis based on abstract using full-text searching in Library Information Systems Department of Electrical Engineering State University of Jakarta. The feature fulltext search system is developed to facilitate students in finding undergraduate thesis based on abstract. By using the select clause MATCH () ... Against, the data can be searched ordered by relevancy value. The research was conducted in the laboratory of multimedia Department of Electrical Engineering Faculty of Engineering, State University of Jakarta from September 2012 to December 2012. Development cycle of full-text search system conceptually consists of the design and analysis of systems, retrieval system development, integration of full-text search system to test SIPEL SIPEL 2013 and 2013. Full-text search system stores abstract 229 thesis abstract (undergraduate thesis, comprehensive, and TA) using XAMPP web server applications. Library Information Systems Department of Electrical Engineering State University of Jakarta (SIPEL 2013) has successfully integrated the SIPEL 2012 by adding databases and full-text search feature. The research use scripting language PHP and the MySQL database and worked on the Linux operating system by using a text editor Bluefish Editor and XAMPP as localhost server.

Two important approaches in multimedia information retrieval are classification and the ranking of the retrieved results. The technique of performing classification using Association Rule Mining (ARM) has been utilized to detect the high-level features from the video, taking advantages of its high efficiency and accuracy. Motivated by the fact that the users are only interested in the top-ranked relevant results, ranking strategies have been adopted to sort the retrieved results. In this paper, an effective and efficient video high-level semantic retrieval framework that utilizes associations and correlations to retrieve and rank the high-level features is developed. The n-feature-value pair rules are generated using a combined measure based on (1) the existence of the (n - 1)-feature-value pairs, where n is larger than 1, (2) the correlation between different n-feature-value pairs and the concept classes through Multiple Correspondence Analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intra-similarity. The final association classification rules are selected by using the calculated similarity values. Then our proposed ranking process uses the scores that integrate the correlation and similarity values to rank the retrieved results. To show the robustness of the proposed framework, experiments with 15 high-level features (concepts) and benchmark data sets from TRECVID and comparisons with 6 other well-known classifiers are presented. Our proposed framework achieves promising performance and outperforms all the other classifiers. Moreover, the final ranked retrieved results are evaluated by the mean average precision measure, which is commonly used for performance evaluation in the TRECVID community.

In China, the mine is facing a stern challenge over its environmental protection, the limitation on its structure and function within its subsystem, optimization of its limited manpower, financial and material resources and its sustainable development. Digital mine is come up with to deal with all these problems. The Digital Mine can be liken to “a logistics supply chain”, the basic characteristic is the high-speed network, with broadband and two-way communication system, used as “path map”, which shall make sure the fast delivery of all the data within all the relevant enterprises in the country; It consists of vehicles, which refers to the techniques of Mine CAD, virtual reality, mine simulation, scientific calculation, artificial intelligence, visualization and office automation; goods, which refers to mine data and mind application model; package, which refers to 3DGM(3-Dimensional Geographical Model) and data mining; security system, which refers to the collection and renewal system of mine data; and dispatching system, which refers to MGIS(Mine Geographical Information System), the common carrier of the entire information and office decisions, controlling the use and operation of all vehicles as well as all of the goods production and the package system. The basic structure of the Digital Mine is composed of two parts: digital ground and digital mine. The digital ground is a management information system based on the EPR (Enterprise Resource Planning) and spatial information infrastructure and information system based on 3S technology and computer network. The digital mine regards the mine geology and surveying data as basic information data for spatial positioning, furthermore, inputting other relevant information if necessary, such as mining working-face, excavating working-face, underground chamber, mechanical and electronic equipments, ventilation and safety device, underground pipeline and communication and others, forming a spatial database. Thus, the entire mine’s information system of management and service and decision support system is established. The Digital Mine is a huge systematic project, involving 3S (GIS, GPS, RS), IT (Information technology), mine science, virtual reality technology and visualization technology. Based on computers and network communication, the Digital Mine realizes the digitization of storing, transporting, expressing and applying of all the relevant spatial data and attribute data, including mine construction, exploration, development, mining, environmental protection and control. In addition, it is also a huge artificial intellectual system that integrates digital construction, digital exploration, digital mining, digital environmental protection and digital forecasting based on data dictionary technology, data warehouse technology, WebGIS, virtual reality technology, multimedia technology, CASE technology and artificial intelligence technology.

Abstract. The mission of digital Cultural Heritage (CH) repositories goes beyond engagement and dissemination; their research data should be approached and correlated spatially and semantically in the service of conservation and accessibility. Therefore, a flexible system is needed to expand, reuse and repurpose their content, addressing online collaboration, real time progress and ease of use. Towards this direction, the “METEORA” web-based platform is a novel and holistic approach to the organization, management and visualization of the 3D and 2D documentation products of CH sites. It integrates a multi-scale 3D viewer based on the 3DHOP framework, personalized information access and interactive tools for data retrieval and presentation. Emphasis is given in the development of a mid-level interface for creating, updating and maintaining 3DHOP’s functionalities and customizing information based on the user’s scientific specialty and field of interest. The proposed admin control panel updates the state of the current visualization, allows the integration of 3D models and multimedia and automates the creation of clickable points of interest on top of the 3D surfaces. It is based on a MySQL Relational Database Management System (RDBMS) that supports the back-end User Interface (UI) with create, read, update and delete (CRUD) management functions of a typical RESTful service. Moreover, all the control mechanisms as well as the procedures of authentication, authorization and encryption of the credentials of the certified users are configured through the PHP scripting language and JSON files. The resulting platform achieves maximum adaptability in digital archiving of heterogeneous datasets and has increasing efficiency in representing all types of data supported by 3DHOP.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. 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In recent years the growth of the World Wide Web exceeded all expectations. Today there are several billions of HTML documents, pictures and other multimedia files available via internet and the number is still rising. But considering the impressive variety of the web, retrieving interesting content has become a very difficult task.So, the World Wide Web is a fertile area for data mining research.Web mining is a research topic which combines two of the activated research areas: Data Mining and World Wide Web. Web mining research relates to several research communities such as Database, information Retrieval and Artificial intelligence, visualization.This paper reviews the research and application issues in web mining besides proving an overall view of Web mining.

With the development of computer technology and network communication technology and multimedia technology,database technology has become an important technical means and software technology for organizing and managinglarge amount of data in information society. It is the foundation of network information management system. Itnot only has a complete theoretical basis, and with the rapid development of hardware technology and softwaretechnology, its application is more and more widely. However, the rapid development of information technology isthat people face such a situation: CAD / CAM, CIMS, CASE, GIS and other applications require the managementof increasingly complex information, such as multimedia data, such as multimedia data, spatial data, scientifi c data,Data and other data types, these data compared with the traditional type, not only the type of complex, diverse forms,and storage, transmission, operation and retrieval methods also have many new features, new requirements, on theone hand, the need for database technology and database systems To manage these complex data in order to obtainthe database system has many of the data management functions, such as query, retrieval, recovery, concurrencycontrol, integrity, storage management. On the other hand, due to the vigorous development of network technology,information processing has long been from a single user environment to the development of the network environment,this environment for resources, information sharing provides a great convenience, and because of multi-machine worktogether to greatly improve the work , The combination of the two requires that the database management system beable to manage complex data objects in a networked environment or heterogeneous environment. Traditional DBMSscannot eff ectively handle complex multimedia data, thus requiring the use of new multimedia indexing and retrievaltechniques. Multimedia database technology is an important part of computer technology, it can simultaneously acquire,process, edit, store and display text, sound, video, graphics and other diff erent media, while it has diversity, integrationand interactivity and so on. Only to solve the key technical issues of multimedia, can be widely used to promote itsfaster development.

World Wide Web has become a powerful platform for retrieval and storage of information. It is a collection of text, image and multimedia files in structured, semi structured and unstructured form. These tremendous volumes of information cannot be processed so simply. An efficient and useful algorithm is required to discover information from these data. Text mining is a method for extracting meaningful information from large volume of data. Unstructured text is easily processed by humans but it is harder for machines. Text mining task involve methods such as tokenization, feature extraction and clustering.

Information retrieval is one of the important areas of research with highest scope for data mining combined with machine learning. The proposed research focus on visual information retrieval by applying machine learning techniques. The usage of multimedia data such as text, images, videos are abundantly increasing day by day in this smart era. Also the need for information classification and retrieval are getting exponential demands to fulfill the research and end user requirements. The tech giants are conducting their researches to develop efficient retrieval systems for videos. Video retrieval is considered to be the toughest and challenging research in the recent times. Due to large storage space, lengthy play time, multiple sequence of frames, spatial temporal challenges, lack of visual relevancy, less hardware and processing support. The proposed visual information retrieval has got higher scope of research with the above listed problems.

With the immense growth in the multimedia contents for education and other purposes, the availability of the video contents has also increased Nevertheless, the retrieval of the content is always a challenge. The identification of two video contents based on internal content similarity highly depends on extraction of key frames and that makes the process highly time complex. In the recent time, many of research attempts have tried to approach this problem with the intention to reduce the time complexity using various methods such as video to text conversion and further analysing both extracted text similarity analysis. Regardless to mention, this strategy is again language dependent and criticised for various reasons like local language dependencies and language paraphrase dependencies. Henceforth, this work approaches the problem with a different dimension with reduction possibilities of the video key frames using adaptive similarity. The proposed method analyses the key frames extracted from the library content and from the search video data based on various parameters and reduces the key frames using adaptive similarity. Also, this work uses machine learning and parallel programming algorithms to reduce the time complexity to a greater extend. The final outcome of this work is a reduced time complex algorithm for video data-based search to video content retrieval. The work demonstrates a nearly 50% reduction in the key frame without losing information with nearly 70% reduction in time complexity and 100% accuracy on search results.

TV stream is a major source of multimedia data. The proposed method aims to enable a good exploitation of this source of video by multimedia services social community, and video-sharing platforms In this work, we propose an approach to the automatic topics segmentation of news video. The originality of the approach is the use of Clustering of Histogram of Orientation Gradients (HOG) faces as prior knowledge. This knowledge is modeled as images which governs the structuring of TV stream content. This structuring is carried out on two levels. The first consists in the identification of anchorperson by Single-Linkage Clustering of HOG faces. The second level aims to identify the topics of news program due to the large audience because of the pertinent information they contain. Experiments comparing the proposed technique to similar works were carried out on the TREC Video Retrieval Evaluation (TRECVID) 2003 database. The results show significant improvements to TV news structuring exceeding 96 %.

The socioeconomic and environmental attributes that characterize the environmental thought are the elements used to design a methodology to evaluate the substantive functions of teaching and research in the university environment, in a public higher education institution in Mexico. The proposed methodology is based on tools and instruments to generate knowledge of big data information, making use of: Information and Communication Technologies; of the principles of multivariate data management that consider the distinctive uncertainty of environmental phenomena; and the generation of multidisciplinary databases to apply data mining and obtain information with a systemic approach. The proposed tool with its mixed approach applies to the operation and multimedia text database study and from there the KDD process knowledge is acquired. The application context of the proposed methodology is the information in text form, published electronically, in the educational offer of the 2017 undergraduate and postgraduate admissions at the Benemérita Universidad Autónoma de Puebla (BUAP).