121,324 resources related to Databases
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.
All fields of satellite, airborne and ground remote sensing.
IEEE INFOCOM solicits research papers describing significant and innovative researchcontributions to the field of computer and data communication networks. We invite submissionson a wide range of research topics, spanning both theoretical and systems research.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
IEE Colloquium on Distributed Databases, 1992
2015 Third World Conference on Complex Systems (WCCS), 2015
The main contribution of this paper is to present a survey of different approaches and techniques that map fuzzy XML schemas to fuzzy relational databases or fuzzy object oriented databases. Moreover, it presents different fuzzy models and XML data models. In addition, the integration process of fuzzy techniques in different databases has been discussed among several categories of data modeling ...
IEE Colloquium on Intelligent Image Databases, 1996
In this paper, a temporal object-oriented approach has been explored for data modeling and management in sequential image databases. "The wood panel deformation measurement and analysis database" is taken as a case study for understanding this sort of complex database application. A generalized temporal object-oriented data model has been presented in which the object- oriented modeling capability supports the integration ...
2010 International Conference on Complex, Intelligent and Software Intensive Systems, 2010
In this paper, our research objective is to develop a database virtualization technique so that data analysts or other users who apply data mining methods to their jobs can use all ubiquitous databases in the Internet as if they were recognized as a single database, thereby helping to reduce their workloads such as data collection from the Internet databases and ...
Datalog and Logic Databases, None
The use of logic in databases started in the late 1960s. In the early 1970s Codd formalized databases in terms of the relational calculus and the relational algebra. A major influence on the use of logic in databases was the development of the field of logic programming. Logic provides a convenient formalism for studying classical database problems and has the ...
Ronald Fagin: 2012 IEEE Computer Society W. Wallace McDowell Award Winner
Time-series Workloads and Implications for Time-series Databases - Michael Freedman - IEEE Sarnoff Symposium, 2019
A perspective shift from Fuzzy logic to Neutrosophic Logic - Swati Aggarwal
The main contribution of this paper is to present a survey of different approaches and techniques that map fuzzy XML schemas to fuzzy relational databases or fuzzy object oriented databases. Moreover, it presents different fuzzy models and XML data models. In addition, the integration process of fuzzy techniques in different databases has been discussed among several categories of data modeling and querying. This survey will support the future research and development work as well as raising the awareness for the presented approaches.
In this paper, a temporal object-oriented approach has been explored for data modeling and management in sequential image databases. "The wood panel deformation measurement and analysis database" is taken as a case study for understanding this sort of complex database application. A generalized temporal object-oriented data model has been presented in which the object- oriented modeling capability supports the integration of data, text and images, timestamps support the sequential images, and methods are used for data processing. A query algebra has been defined for the model. This algebra allows query and data manipulation. The database support for automatic sequential image deformation analysis has also been investigated.
In this paper, our research objective is to develop a database virtualization technique so that data analysts or other users who apply data mining methods to their jobs can use all ubiquitous databases in the Internet as if they were recognized as a single database, thereby helping to reduce their workloads such as data collection from the Internet databases and data cleansing works. In this study, firstly we examine XML scheme advantages and propose a database virtualization method by which such ubiquitous databases as relational databases, object-oriented databases, and XML databases are usable, as if they all behaved as a single database. Next, we show the method of virtualization of ubiquitous databases can describe ubiquitous database schema in a unified fashion using the XML schema. Moreover, it consists of a high-level concept of distributed database management of the same type and of different types, and also of a location transparency feature. Finally, we propose a database incompatibility trouble-recovery technique for use in a virtualized ubiquitous database use environment.
The use of logic in databases started in the late 1960s. In the early 1970s Codd formalized databases in terms of the relational calculus and the relational algebra. A major influence on the use of logic in databases was the development of the field of logic programming. Logic provides a convenient formalism for studying classical database problems and has the important property of being declarative, that is, it allows one to express what she wants rather than how to get it. For a long time, relational calculus and algebra were considered the relational database languages. However, there are simple operations, such as computing the transitive closure of a graph, which cannot be expressed with these languages. Datalog is a declarative query language for relational databases based on the logic programming paradigm. One of the peculiarities that distinguishes Datalog from query languages like relational algebra and calculus is recursion, which gives Datalog the capability to express queries like computing a graph transitive closure. Recent years have witnessed a revival of interest in Datalog in a variety of emerging application domains such as data integration, information extraction, networking, program analysis, security, cloud computing, ontology reasoning, and many others. The aim of this book is to present the basics of Datalog, some of its extensions, and recent applications to different domains.
Ongoing research into intelligent image databases continues to produce new and innovative techniques for the browsing and retrieval of digital images, but these techniques are generally not applicable to moving image (video) databases. Video sequences, by their very nature, contain a temporal element. This, coupled with the high volume of data needed to represent even small segments of video, makes many of the image database techniques impractical. We have developed a technique which eliminates the temporal content of video, and reduces the amount of data required for its representation, by automatically identifying and extracting key frames in a video sequence. The result is a 'storyboard' consisting of a number of video stills, each one chosen to best represent a shot in the video sequence, together with information about each shot, such as camera motion, start frame, duration, etc. The storyboard provides a pictorial index to the video sequence from which it is generated, and the task of locating a particular sequence in a video database is thus reduced to the task of locating one or more still images from a storyboard database. This can be achieved by image retrieval techniques such as query-by- image content (QBIC). In addition, the storyboard provides a summary of the video sequence, in which the semantic content is retained, but with far greater economy as regards the amount of data and time needed to represent it. Hence any 'hit' found by the database query can be presented to the user as a storyboard rather than the video sequence itself, allowing quick, low bandwidth browsing.
Knowledge discovery in databases is the process of applying statistical, machine learning and other techniques to conventional database systems. Our survey in knowledge discovery systems has indicated that up to date there is no knowledge discovery system to deal with temporal databases. In this paper, we first give a brief description of temporal database systems and then we present some examples to show how the ORES temporal database management system could provide the necessary functionality to infer accurate and valuable knowledge from temporal databases. In particular, we discuss three common classes of database mining problems involving classifications, associations and sequences. We give a short description of our overall framework for knowledge discovery under research. The work focuses on two areas and their integration: on one side, data mining as a technique to increase the quality of data, and on the other side, temporal databases as a technique to keep the history of data. We believe that their integration will lead to even higher quality data.
The process of knowledge discovery applied in distributed databases implies finding useful knowledge from mining data sets stored in real implementations of distributed databases. Distributed Databases represents a software system that allows a multitude of applications to access the data stored in local or remote databases. In this scenario, the data distribution is achieved through the process of replication. Nowadays many solutions for storing the data are available: relational distributed Database Management Systems (DBMS), NoSQL storing solutions, NewSQL storing solutions, graph oriented databases, object oriented databases, object-relational databases, etc. The present study analyzes the most commonly used storing solution: the relational model. The replication topology used in the related experiments was the classical publisher-subscriber topology. The distribution of data is made from the publisher system. The present work studies the interaction between the most suited distributed data mining architecture (Distributed Committee Machines) for mining distributed data and real relational distributed databases. The chosen Data Mining task is the classification one. Distributed Committee Machines are a group of neuronal networks working in a distributed procedure to obtain an improved classification performance compared to a single neural structure. In these experiments we used the classical multilayer perceptron trained with the backpropagation algorithm. The execution performance of the Distributed Committee Machine is analyzed, based on some of the most used types of replication in relational databases: snapshot replication, merge replication, transactional replication, and transactional with queued updating replication. For all these types of replications the execution performances (distributed speedup and distributed efficiency) of the entire system is also analyzed. These results are useful to numerous research fields: adaptive e-learning applications, medical diagnosis, artificial intelligence, business management, etc.
The concept of knowledge discovery in databases (KDD) is an important future challenge for the different communities of machine learning (ML), statistics and databases. Although the aim of this new research area seems to be identified (the extraction of implicit, previously unknown, and potentially useful information from data), many criticise the lack of precision of this definition and, in particular, its similarity with the aim generally proclaimed for ML. We first recall the main purposes of ML and the proposed aims of KDD. That underlines the main new problems that KDD proposes to tackle which ML has not yet solved. Secondly, we specify the differences between both domains by answering specific comments that G. Piatetsky-Shapiro (1992, 1993) has made concerning this comparison of ML and KDD. It is important to clearly distinguish KDD from ML in order on the one hand to focus future ML research on the extension of current ML techniques aiming at larger KDD problems, and on the other hand for KDD researchers to know which ML techniques are best adapted to particular KDD tasks. We must, in the future, identify tasks that have not yet been sufficiently explored and then look for techniques developed in different scientific fields (in ML as well as in other fields) to solve comparable tasks.
Advances in digital storage technology and processing speed have made feasible the creation of large databases with rapid access to individual items stored therein. While many databases still comprise textual and numeric information, an increasing number are genuinely multi-media. A desirable - and attainable - goal of such diverse databases is to retrieve information from them in a manner that is minimally constrained by the medium in which the query is started. Consider a purely imaginary example: a database of the world's endangered species. For any single entry, we might expect any or all of the following: Still images, e.g. of the species in its natural habitat; Video clips, showing aspects of behaviour; Sound recordings; Textual descriptions; Numeric data, such as statistics. If we are navigating this database, and arrive at one of these items of information, we naturally expect to be able to move smoothly to any of the others that relate to the same species. In this paper, we will concentrate on visual methods for navigating such multi-media collections but, to begin with, we briefly review some of the available strategies for searching large databases.