Multimedia Data Mining Concept

2008 ◽  
pp. 3611-3620
Author(s):  
Janusz Swierzowicz
Keyword(s):  
Digital Media ◽  
Data Storage ◽  
Multimedia Data ◽  
Digital Data ◽  
Data Sets ◽  
Still Images ◽  
Three Dimensional Objects ◽  
Distributed Query ◽  
High Voice ◽  
The 1960S

The development of information technology is particularly noticeable in the methods and techniques of data acquisition, high-performance computing, and bandwidth frequency. According to a newly observed phenomenon, called a storage low (Fayyad & Uthurusamy, 2002), the capacity of digital data storage is doubled every 9 months with respect to the price. Data can be stored in many forms of digital media, for example, still images taken by a digital camera, MP3 songs, or MPEG videos from desktops, cell phones, or video cameras. Such data exceeds the total cumulative handwriting and printing during all of recorded human history (Fayyad, 2001). According to current analysis carried out by IBM Almaden Research (Swierzowicz, 2002), data volumes are growing at different speeds. The fastest one is Internet-resource growth: It will achieve the digital online threshold of exabytes within a few years (Liautaud, 2001). In these fast-growing volumes of data environments, restrictions are connected with a human’s low data-complexity and dimensionality analysis. Investigations on combining different media data, multimedia, into one application have begun as early as the 1960s, when text and images were combined in a document. During the research and development process, audio, video, and animation were synchronized using a time line to specify when they should be played (Rowe & Jain, 2004). Since the middle 1990s, the problems of multimedia data capture, storage, transmission, and presentation have extensively been investigated. Over the past few years, research on multimedia standards (e.g., MPEG-4, X3D, MPEG-7) has continued to grow. These standards are adapted to represent very complex multimedia data sets; can transparently handle sound, images, videos, and 3-D (three-dimensional) objects combined with events, synchronization, and scripting languages; and can describe the content of any multimedia object. Different algorithms need to be used in multimedia distribution and multimedia database applications. An example is an image database that stores pictures of birds and a sound database that stores recordings of birds (Kossmann, 2000). The distributed query that asks for “top ten different kinds of birds that have black feathers and a high voice” is described there by Kossmann (2000, p.436).

Multimedia Data Mining Concept

2011 ◽  
pp. 696-703
Author(s):  
Janusz Swierzowicz
Keyword(s):  
Digital Media ◽  
Data Storage ◽  
Multimedia Data ◽  
Digital Data ◽  
Data Sets ◽  
Still Images ◽  
Three Dimensional Objects ◽  
Distributed Query ◽  
High Voice ◽  
The 1960S

The development of information technology is particularly noticeable in the methods and techniques of data acquisition, high-performance computing, and bandwidth frequency. According to a newly observed phenomenon, called a storage low (Fayyad & Uthurusamy, 2002), the capacity of digital data storage is doubled every 9 months with respect to the price. Data can be stored in many forms of digital media, for example, still images taken by a digital camera, MP3 songs, or MPEG videos from desktops, cell phones, or video cameras. Such data exceeds the total cumulative handwriting and printing during all of recorded human history (Fayyad, 2001). According to current analysis carried out by IBM Almaden Research (Swierzowicz, 2002), data volumes are growing at different speeds. The fastest one is Internet-resource growth: It will achieve the digital online threshold of exabytes within a few years (Liautaud, 2001). In these fast-growing volumes of data environments, restrictions are connected with a human’s low data-complexity and dimensionality analysis. Investigations on combining different media data, multimedia, into one application have begun as early as the 1960s, when text and images were combined in a document. During the research and development process, audio, video, and animation were synchronized using a time line to specify when they should be played (Rowe & Jain, 2004). Since the middle 1990s, the problems of multimedia data capture, storage, transmission, and presentation have extensively been investigated. Over the past few years, research on multimedia standards (e.g., MPEG-4, X3D, MPEG-7) has continued to grow. These standards are adapted to represent very complex multimedia data sets; can transparently handle sound, images, videos, and 3-D (three-dimensional) objects combined with events, synchronization, and scripting languages; and can describe the content of any multimedia object. Different algorithms need to be used in multimedia distribution and multimedia database applications. An example is an image database that stores pictures of birds and a sound database that stores recordings of birds (Kossmann, 2000). The distributed query that asks for “top ten different kinds of birds that have black feathers and a high voice” is described there by Kossmann (2000, p.436).

Multimedia Data Mining Trends and Challenges

2009 ◽  
pp. 965-971
Author(s):  
Janusz Swierzowicz
Keyword(s):  
Data Mining ◽  
Digital Media ◽  
Digital Camera ◽  
Unsupervised Classification ◽  
Multimedia Data ◽  
Digital Data ◽  
Still Images ◽  
Multimedia Data Mining ◽  
Methods And Techniques ◽  
Critical Issues

The development of information technology is particularly noticeable in the methods and techniques of data acquisition. Data can be stored in many forms of digital media, for example, still images taken by a digital camera, MP3 songs, or MPEG videos from desktops, cell phones, or video cameras. Data volumes are growing at different speeds with the fastest Internet and multimedia resource growth. In these fast growing volumes of digital data environments, restrictions are connected with a human’s low data complexity and dimensionality analysis. The article begins with a short introduction to data mining, considering different kinds of data, both structured as well as semistructured and unstructured. It emphasizes the special role of multimedia data mining. Then, it presents a short overview of data mining goals, methods, and techniques used in multimedia data mining. This section focuses on a brief discussion on supervised and unsupervised classification, uncovering interesting rules, decision trees, artificial neural networks, and rough-neural computing. The next section presents advantages offered by multimedia data mining and examples of practical and successful applications. It also contains a list of application domains. The following section describes multimedia data mining critical issues, summarizes main multimedia data mining advantages and disadvantages, and considers some predictive trends.

Spectral Methods for Data Clustering

2011 ◽  
pp. 1037-1042
Author(s):  
Wenyuan Li
Keyword(s):  
Data Storage ◽  
Dna Microarrays ◽  
Clustering Algorithms ◽  
High Volume ◽  
Digital Data ◽  
The Internet ◽  
Data Sets ◽  
Time Data ◽  
Low Dimensionality ◽  
Tremendous Amount

With the rapid growth of the World Wide Web and the capacity of digital data storage, tremendous amount of data are generated daily from business and engineering to the Internet and science. The Internet, financial real-time data, hyperspectral imagery, and DNA microarrays are just a few of the common sources that feed torrential streams of data into scientific and business databases worldwide. Compared to statistical data sets with small size and low dimensionality, traditional clustering techniques are challenged by such unprecedented high volume, high dimensionality complex data. To meet these challenges, many new clustering algorithms have been proposed in the area of data mining (Han & Kambr, 2001).

Revisiting FPGA Implementation of Digital Filters and Exploring Approximate Computing on Biomedical Signals

2020 ◽  
Vol 10 (9) ◽  
pp. 2000-2004
Author(s):  
Wang Hui ◽  
Gong Chang ◽  
S. Saravanan ◽  
V. Gomathi ◽  
R. Valarmathi ◽  
...  
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Noise Removal ◽  
Multimedia Data ◽  
Digital Data ◽  
Functional Verification ◽  
Approximate Computing ◽  
Human Beings ◽  
Volume Data ◽  
Simulation Results

In recent years, the approximate computing becomes popular in the era of VLSI (very large scale integration) domain to arrive better power, area, and delay outcomes at the cost of lower precision loss. Also, the human beings are not so intelligent to see/observe/listen the processed digital data; means even if some of the data loss occurs human beings are unable to notice them. This behavior set the engineers to research on approximate computing which are very useful in the multimedia data processing, data communications, high-volume data storage, etc. In this study, the experiments such as hum-noise removal, filters on QRS detection are implemented on an Altera FPGA EP4CEF29C7 device using Quartus II 13.1 synthesis software tool and the simulation results on device utilization reports, the speed and the power are obtained. Simulation results reveal that the approximate computational filters offer better power, area, and speed results than the conventional ones. Also, Matlab 9.4 (R2018a) simulation was used to carry out the functional verification of the actual and approximate filters.

New Trends in Digital Data Storage for the Internet of Things

2018 ◽  
Vol 6 (3) ◽  
pp. 359-363
Author(s):  
A. Saxena ◽  
◽  
S. Sharma ◽  
S. Dangi ◽  
A. Sharma ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Digital Data ◽  
The Internet ◽  
Digital Data Storage ◽  
The Internet Of Things

Deoxyribonucleic Acid as a Tool for Digital Information Storage: An Overview

2019 ◽  
Vol 15 (01) ◽  
pp. 1-8
Author(s):  
Ashish C Patel ◽  
C G Joshi
Keyword(s):  
Data Storage ◽  
Dna Sequences ◽  
Consensus Sequence ◽  
Random Access ◽  
Information Storage ◽  
Digital Data ◽  
Digital Information ◽  
Multiple Sequence ◽  
Digital World ◽  
Digital File

Current data storage technologies cannot keep pace longer with exponentially growing amounts of data through the extensive use of social networking photos and media, etc. The "digital world” with 4.4 zettabytes in 2013 has predicted it to reach 44 zettabytes by 2020. From the past 30 years, scientists and researchers have been trying to develop a robust way of storing data on a medium which is dense and ever-lasting and found DNA as the most promising storage medium. Unlike existing storage devices, DNA requires no maintenance, except the need to store at a cool and dark place. DNA has a small size with high density; just 1 gram of dry DNA can store about 455 exabytes of data. DNA stores the informations using four bases, viz., A, T, G, and C, while CDs, hard disks and other devices stores the information using 0’s and 1’s on the spiral tracks. In the DNA based storage, after binarization of digital file into the binary codes, encoding and decoding are important steps in DNA based storage system. Once the digital file is encoded, the next step is to synthesize arbitrary single-strand DNA sequences and that can be stored in the deep freeze until use.When there is a need for information to be recovered, it can be done using DNA sequencing. New generation sequencing (NGS) capable of producing sequences with very high throughput at a much lower cost about less than 0.1 USD for one MB of data than the first sequencing technologies. Post-sequencing processing includes alignment of all reads using multiple sequence alignment (MSA) algorithms to obtain different consensus sequences. The consensus sequence is decoded as the reversal of the encoding process. Most prior DNA data storage efforts sequenced and decoded the entire amount of stored digital information with no random access, but nowadays it has become possible to extract selective files (e.g., retrieving only required image from a collection) from a DNA pool using PCR-based random access. Various scientists successfully stored up to 110 zettabytes data in one gram of DNA. In the future, with an efficient encoding, error corrections, cheaper DNA synthesis,and sequencing, DNA based storage will become a practical solution for storage of exponentially growing digital data.

scholarly journals Smarter Open Government Data for Society 5.0: Are Your Open Data Smart Enough?

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5204
Author(s):  
Anastasija Nikiforova
Keyword(s):  
Industry 4.0 ◽  
Economic Value ◽  
Open Data ◽  
Digital Data ◽  
Open Government ◽  
Data Sets ◽  
Time Data ◽  
Open Government Data ◽  
Information And Communication ◽  
Government Data

Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users’ intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These “smarter” open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of “smarter” data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users’ needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world.

Sign in / Sign up

Export Citation Format

Share Document