社区
下载资源悬赏专区
帖子详情
data semantics on the web.pdf下载
weixin_39820835
2021-01-19 07:01:01
data semantics on the web.pdf
相关下载链接:
//download.csdn.net/download/jingyun546/7320959?utm_source=bbsseo
...全文
6
回复
打赏
收藏
data semantics on the web.pdf下载
data semantics on the web.pdf 相关下载链接://download.csdn.net/download/jingyun546/7320959?utm_source=bbsseo
复制链接
扫一扫
分享
转发到动态
举报
写回复
配置赞助广告
用AI写文章
回复
切换为时间正序
请发表友善的回复…
发表回复
打赏红包
data
semantics
on the
web
.
pdf
data
semantics
on the
web
.
pdf
Big
Data
Analytics with Applications in Insider Threat Detection-CRC(2018).
pdf
Recent developments in information systems technologies have resulted in computerizing many applications in various business areas.
Data
has become a critical resource in many organizations, and therefore, ef cient access to
data
, sharing the
data
, extracting information from the
data
, and making use of the information has become an urgent need. As a result, there have been many efforts on not only integrating the various
data
sources scattered across several sites, but extracting infor- mation from these
data
bases in the form of patterns and trends and carrying out
data
analytics has also become important. These
data
sources may be
data
bases managed by
data
base management systems, or they could be
data
warehoused in a repository from multiple
data
sources. The advent of the World Wide
Web
in the mid-1990s has resulted in even greater demand for managing
data
, information, and knowledge effectively. During this period, the services paradigm was conceived which has now evolved into providing computing infrastructures, software,
data
- bases, and applications as services. Such capabilities have resulted in the notion of cloud computing. Over the past 5 years, developments in cloud computing have exploded and we now have several companies providing infrastructure software and application computing platforms as services. As the demand for
data
and information management increases, there is also a critical need for maintaining the security of the
data
bases, applications, and information systems.
Data
, informa- tion, applications, the
web
, and the cloud have to be protected from unauthorized access as well as from malicious corruption. The approaches to secure such systems have come to be known as cyber security. The signi cant developments in
data
management and analytics,
web
services, cloud computing, and cyber security have evolved into an area called big
data
management and analytics (BDMA) as well as big
data
security and privacy (BDSP). The U.S. Bureau of Labor and Statistics de nes big
data
as a collection of large
data
sets that cannot be analyzed with normal statistical methods. The
data
sets can represent numerical, textual, and multimedia
data
. Big
data
is popularly de ned in terms of ve Vs: volume, velocity, variety, veracity, and value. BDMA requires handling huge volumes of
data
, both structured and unstructured, arriving at high velocity. By harnessing big
data
, we can achieve breakthroughs in several key areas such as cyber security and healthcare, resulting in increased productivity and pro tability. Not only do the big
data
systems have to be secure, the big
data
analytics have to be applied for cyber security applications such as insider threat detection. This book will review the developments in topics both BDMA and BDSP and discuss the issues and challenges in securing big
data
as well as applying big
data
techniques to solve problems. We will focus on a speci c big
data
analytics technique called stream
data
mining as well as approaches to applying this technique to insider threat detection. We will also discuss several experimental systems, infrastructures and education programs we have developed at The University of Texas at Dallas on both BDMA and BDSP. We have written two series of books for CRC Press on
data
management/
data
mining and
data
security. The rst series consist of 10 books. Book #1 (
Data
Management Systems Evolution and Interoperation) focused on general aspects of
data
management and also addressed interoperability and migration. Book #2 (
Data
Mining: Technologies, Techniques, Tools, and Trends) discussed
data
mining. It essentially elaborated on Chapter 9 of Book #1. Book #3 (
Web
Data
Management and Electronic Commerce) discussed
web
data
base technologies and discussed e-commerce as an application area. It essentially elaborated on Chapter 10 of Book #1. Book #4 (Managing and Mining Multimedia
Data
bases) addressed both multimedia
data
base management and multimedia
data
mining. It elaborated on both Chapter 6 of Book #1 (for multimedia
data
base management) xxiii xxiv Preface and Chapter 11 of Book #2 (for multimedia
data
mining). Book #5 (XML,
Data
bases and the Semantic
Web
) described XML technologies related to
data
management. It elaborated on Chapter 11 of Book #3. Book #6 (
Web
Data
Mining and Applications in Business Intelligence and Counter- terrorism) elaborated on Chapter 9 of Book #3. Book #7 (
Data
base and Applications Security) examined security for technologies discussed in each of our previous books. It focuses on the tech- nological developments in
data
base and applications security. It is essentially the integration of Information Security and
Data
base Technologies. Book #8 (Building Trustworthy Semantic
Web
s) applies security to semantic
web
technologies and elaborates on Chapter 25 of Book #7. Book #9 (Secure Semantic Service-Oriented Systems) is an elaboration of Chapter 16 of Book #8. Book #10 (Developing and Securing the Cloud) is an elaboration of Chapters 5 and 25 of Book #9. Our second series of books at present consists of four books. Book #1 is Design and Implementation of
Data
Mining Tools. Book #2 is
Data
Mining Tools for Malware Detection. Book #3 is Secure
Data
Provenance and Inference Control with Semantic
Web
. Book #4 is Analyzing and Securing Social Networks. Book #5, which is the current book, is Big
Data
Analytics with Applications in Insider Threat Detection. For this series, we are converting some of the practical aspects of our work with students into books. The relationships between our texts will be illus- trated in Appendix A. ORGANIZATION OF THIS BOOK This book is divided into ve parts, each describing some aspect of the technology that is relevant to BDMA and BSDP. The major focus of this book will be on stream
data
analytics and its applica- tions in insider threat detection. In addition, we will also discuss some of the experimental systems we have developed and provide some of the challenges involved. Part I, consisting of six chapters, will describe supporting technologies for BDMA and BDSP including
data
security and privacy,
data
mining, cloud computing and semantic
web
. Part II, consisting of six chapters, provides a detailed overview of the techniques we have developed for stream
data
analytics. In particular, we will describe our techniques on novel class detection for
data
streams. Part III, consisting of nine chapters, will discuss the applications of stream analytics for insider threat detection. Part IV, consisting of six chapters, will discuss some of the experimental systems we have developed based on BDMA and BDSP. These include secure query processing for big
data
as well as social media analysis. Part V, consisting of seven chapters, discusses some of the challenges for BDMA and BDSP. In particular, securing the Internet of Things as well as our plans for developing experimental infrastructures for BDMA and BDSP are also discussed.
DATA
, INFORMATION, AND KNOWLEDGE In general,
data
management includes managing the
data
bases, interoperability, migration, ware- housing, and mining. For example, the
data
on the
web
has to be managed and mined to extract information and patterns and trends.
Data
could be in les, relational
data
bases, or other types of
data
bases such as multimedia
data
bases.
Data
may be structured or unstructured. We repeatedly use the terms
data
,
data
management, and
data
base systems and
data
base management systems in this book. We elaborate on these terms in the appendix. We de ne
data
management systems to be systems that manage the
data
, extract meaningful information from the
data
, and make use of the information extracted. Therefore,
data
management systems include
data
base systems,
data
ware- houses, and
data
mining systems.
Data
could be structured
data
such as those found in relational
data
bases, or it could be unstructured such as text, voice, imagery, and video. There have been numerous discussions in the past to distinguish between
data
, information, and knowledge. In some of our previous books on
data
management and mining, we did not attempt to clarify these terms. We simply stated that,
data
could be just bits and bytes or it could convey some meaningful information to the user. However, with the
web
and also with increasing interest in
data
, Preface xxv information and knowledge management as separate areas, in this book we take a different approach to
data
, information, and knowledge by differentiating between these terms as much as possible. For us
data
is usually some value like numbers, integers, and strings. Information is obtained when some meaning or
semantics
is associated with the
data
such as John’s salary is 20K. Knowledge is something that you acquire through reading and learning, and as a result understand the
data
and information and take actions. That is,
data
and information can be transferred into knowledge when uncertainty about the
data
and information is removed from someone’s mind. It should be noted that it is rather dif cult to give strict de nitions of
data
, information, and knowledge. Sometimes we will use these terms interchangeably also. Our framework for
data
management discussed in the appendix helps clarify some of the differences. To be consistent with the terminology in our previ- ous books, we will also distinguish between
data
base systems and
data
base management systems. A
data
base management system is that component which manages the
data
base containing persistent
data
. A
data
base system consists of both the
data
base and the
data
base management system. FINAL THOUGHTS The goal of this book is to explore big
data
analytics techniques and apply them for cyber secu- rity including insider threat detection. We will discuss various concepts, technologies, issues, and challenges for both BDMA and BDSP. In addition, we also present several of the experimental systems in cloud computing and secure cloud computing that we have designed and developed at The University of Texas at Dallas. We have used some of the material in this book together with the numerous references listed in each chapter for graduate level courses at The University of Texas at Dallas on “Big
Data
Analytics” as well on “Developing and Securing the Cloud.” We have also provided several experimental systems developed by our graduate students. It should be noted that the eld is expanding very rapidly with several open source tools and commercial products for managing and analyzing big
data
. Therefore, it is important for the reader to keep up with the developments of the various big
data
systems. However, security cannot be an afterthought. Therefore, while the technologies for big
data
are being developed, it is important to include security at the onset.
Professional XMPP Programming with JavaScript and jQuery.
pdf
XMPP Powers a wIde range of aPPlIcatIons including instant messaging, multi-user chat, voice and video conferencing, collaborative spaces, real-time gaming,
data
synchronization, and even search. Although XMPP started its life as an open, standardized alternative to proprietary instant messaging systems like ICQ and AOL Instant Messenger, it has matured into an extremely robust protocol for all kinds of exciting creations. Facebook uses XMPP technology as part of its chat system. Google uses XMPP to power Google Talk and its exciting new Google Wave protocol. Collecta has built a real-time search engine based extensively on XMPP’s publish-subscribe system. Several
web
browsers are experimenting with XMPP as the basis of their synchronization and sharing systems. Dozens of other companies have XMPP-enabled their
web
applications to provide enhanced user experiences and real-time interaction. The core of XMPP is the exchange of small, structured chunks of information. Like HTTP, XMPP is a client-server protocol, but it differs from HTTP by allowing either side to send
data
to the other asynchronously. XMPP connections are long lived, and
data
is pushed instead of pulled. Because of XMPP’s differences, it provides an excellent companion protocol to HTTP. XMPP-powered
web
applications are to AJAX what AJAX was to the static
web
site; they are the next level of interactiv- ity and dynamism. Where JavaScript and dynamic HTML have brought desktop application features to the
web
browser, XMPP brings new communications possibilities to the
Web
. XMPP has many common social
web
features built in, due to its instant messaging heritage. Contact lists and subscriptions create social graphs, presence updates help users keep track of who is doing what, and private messaging makes communication among users trivial. XMPP also has nearly 300 extensions, providing a broad and useful range of tools on which to build sophisticated applications. With only a handful of the
【HTML5】\HTML5 Cookbook.(高清本文)
pdf
【HTML5】\HTML5 Cookbook.(高清本文)
pdf
With scores of practical recipes you can use in your projects right away, this cookbook helps you gain hands-on experience with HTML5’s versatile collection of elements. You get clear solutions for handling issues with everything from markup
semantics
,
web
forms, and audio and video elements to related technologies such as geolocation and rich JavaScript APIs. Each informative recipe includes sample code and a detailed discussion on why and how the solution works. Perfect for intermediate to advanced
web
and mobile
web
developers, this handy book lets you choose the HTML5 features that work for you—and helps you experiment with the rest. Test browsers for HTML5 support, and use techniques for applying unsupported features Discover how HTML5 makes
web
form implementation much simpler Overcome challenges for implementing native audio and video elements Learn techniques for using HTML5 with ARIA accessibility guidelines Explore examples that cover using geolocation
data
in your applications Draw images, use transparencies, add gradients and patterns, and more with Canvas Bring HTML5 features to life with a variety of advanced JavaScript APIs
Think Julia
If you’re just learning how to program, Julia is an excellent JIT-compiled, dynamically typed language with a clean syntax. This hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch. Designed from the beginning for high performance, Julia is a general-purpose language ideal for not only numerical analysis and computational science but also
web
programming and scripting. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Julia is perfect for students at the high school or college level as well as self-learners and professionals who need to learn programming basics. Start with the basics, including language syntax and
semantics
Get a clear definition of each programming concept Learn about values, variables, statements, functions, and
data
structures in a logical progression Discover how to work with files and
data
bases Understand types, methods, and multiple dispatch Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design and
data
structures through case studies
下载资源悬赏专区
12,795
社区成员
12,333,699
社区内容
发帖
与我相关
我的任务
下载资源悬赏专区
CSDN 下载资源悬赏专区
复制链接
扫一扫
分享
社区描述
CSDN 下载资源悬赏专区
其他
技术论坛(原bbs)
社区管理员
加入社区
获取链接或二维码
近7日
近30日
至今
加载中
查看更多榜单
社区公告
暂无公告
试试用AI创作助手写篇文章吧
+ 用AI写文章