Knowledge-driven enterprises can become more adaptable, dynamic and collaborative by using semantic technologies to integrate openly available data into the ecosystem.
The document discusses a new approach to data analysis called CXAIR that combines search engine technology and business analytics. CXAIR allows business users to explore and analyze large amounts of structured and unstructured data through an intuitive interface without needing technical expertise. It provides capabilities for ad-hoc querying, joining disparate data sources, and dynamically segmenting and clustering data to gain insights. This empowering new approach could help companies better utilize their data assets.
Semantic Web Mining of Un-structured Data: Challenges and OpportunitiesCSCJournals
The management of unstructured data is acknowledged as one of the most critical unsolved problems in data management and business intelligence fields in current times. The major reason for this unresolved problem is primarily because of the actuality that the methods, systems and related tools that have established themselves so successfully converting structured information into business intelligence, simply are ineffective when we try to implement the same on unstructured information. New methods and approaches are very much necessary. It is a known realism that huge amount of information is shared by the organizations across the world over the web. It is, however, significant to observe that this information explosion across the globe has resulted in opening a lot of new avenues to create tools for data management and business intelligence primarily focusing on unstructured data. In this paper, we explore the challenges being faced by information system developers during mining of unstructured data in the context of semantic web and web mining. Opportunities in the wake of these challenges are discussed towards the end of the paper.
Sentiment analysis and classification of tweets using rapid miner toolValarmathi Srinivasan
In today’s world, Social networking sites like Twitter, Facebook are the great source of communication for internet users.
So it becomes an important source for understanding the opinions, views or emotions of people.
Here, the sentiment analysis is been performed of twitter data for the purpose of classification on the views, people have shared on Twitter, which is one of the most used social networking sites nowadays.
Using RapidMiner tool, various operations have been performed on the twitter data such as, Collecting tweets, analyzing sentiments, categorizing tweets and visualizing the sentiment polarity such as positive, negative and neutral to provide better view and the data will be stored in SQLSERVER Database for future use.
The document summarizes James LoBuono's interview about the growing demand for data scientists. Some key points:
- Data science skills are in high demand across industries due to increased data availability from sensors and cloud computing.
- Data scientists are needed to extract useful information from messy, unstructured data sources to aid decision making.
- Programming languages like Python and tools like machine learning are commonly used in data science roles.
- Data science can help solve business problems and unlock opportunities by making decisions based on data analysis rather than intuition alone.
The document introduces the concept of semantic intelligence, which represents the ability to process data and information based on semantic patterns and rules to gain insight. It also represents the ability to harness shared knowledge in a more efficient, automated way across organizations. Semantic intelligence is defined as supporting complex data relationships, combining structured and unstructured data, complex visualization, rules and knowledge collaboration. It has the potential to unify analytic approaches and technologies to provide a more specific set of solution expectations.
Intro to big data and applications - day 1Parviz Vakili
This document provides an overview and introduction to big data and its applications. It defines key concepts related to big data, including the five V's of big data (volume, velocity, variety, veracity, and value). It also discusses where big data comes from, different data types (structured, semi-structured, unstructured), and common applications of big data across different industries. Finally, it introduces concepts of data governance, data strategy, and how big data can support digital transformation.
IRJET- A Novel Approach for Accomplishing Data Reliability and Isolation Safe...IRJET Journal
This document proposes a novel approach called TPDM to accomplish data reliability and isolation in data markets. TPDM uses partially homomorphic encryption and identity-based signatures to enable batch verification, data processing, and result checking while maintaining privacy and data secrecy. The performance of TPDM is evaluated on real datasets and it is shown to achieve desirable properties with low computation and communication overhead for large-scale data markets.
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
The document discusses a new approach to data analysis called CXAIR that combines search engine technology and business analytics. CXAIR allows business users to explore and analyze large amounts of structured and unstructured data through an intuitive interface without needing technical expertise. It provides capabilities for ad-hoc querying, joining disparate data sources, and dynamically segmenting and clustering data to gain insights. This empowering new approach could help companies better utilize their data assets.
Semantic Web Mining of Un-structured Data: Challenges and OpportunitiesCSCJournals
The management of unstructured data is acknowledged as one of the most critical unsolved problems in data management and business intelligence fields in current times. The major reason for this unresolved problem is primarily because of the actuality that the methods, systems and related tools that have established themselves so successfully converting structured information into business intelligence, simply are ineffective when we try to implement the same on unstructured information. New methods and approaches are very much necessary. It is a known realism that huge amount of information is shared by the organizations across the world over the web. It is, however, significant to observe that this information explosion across the globe has resulted in opening a lot of new avenues to create tools for data management and business intelligence primarily focusing on unstructured data. In this paper, we explore the challenges being faced by information system developers during mining of unstructured data in the context of semantic web and web mining. Opportunities in the wake of these challenges are discussed towards the end of the paper.
Sentiment analysis and classification of tweets using rapid miner toolValarmathi Srinivasan
In today’s world, Social networking sites like Twitter, Facebook are the great source of communication for internet users.
So it becomes an important source for understanding the opinions, views or emotions of people.
Here, the sentiment analysis is been performed of twitter data for the purpose of classification on the views, people have shared on Twitter, which is one of the most used social networking sites nowadays.
Using RapidMiner tool, various operations have been performed on the twitter data such as, Collecting tweets, analyzing sentiments, categorizing tweets and visualizing the sentiment polarity such as positive, negative and neutral to provide better view and the data will be stored in SQLSERVER Database for future use.
The document summarizes James LoBuono's interview about the growing demand for data scientists. Some key points:
- Data science skills are in high demand across industries due to increased data availability from sensors and cloud computing.
- Data scientists are needed to extract useful information from messy, unstructured data sources to aid decision making.
- Programming languages like Python and tools like machine learning are commonly used in data science roles.
- Data science can help solve business problems and unlock opportunities by making decisions based on data analysis rather than intuition alone.
The document introduces the concept of semantic intelligence, which represents the ability to process data and information based on semantic patterns and rules to gain insight. It also represents the ability to harness shared knowledge in a more efficient, automated way across organizations. Semantic intelligence is defined as supporting complex data relationships, combining structured and unstructured data, complex visualization, rules and knowledge collaboration. It has the potential to unify analytic approaches and technologies to provide a more specific set of solution expectations.
Intro to big data and applications - day 1Parviz Vakili
This document provides an overview and introduction to big data and its applications. It defines key concepts related to big data, including the five V's of big data (volume, velocity, variety, veracity, and value). It also discusses where big data comes from, different data types (structured, semi-structured, unstructured), and common applications of big data across different industries. Finally, it introduces concepts of data governance, data strategy, and how big data can support digital transformation.
IRJET- A Novel Approach for Accomplishing Data Reliability and Isolation Safe...IRJET Journal
This document proposes a novel approach called TPDM to accomplish data reliability and isolation in data markets. TPDM uses partially homomorphic encryption and identity-based signatures to enable batch verification, data processing, and result checking while maintaining privacy and data secrecy. The performance of TPDM is evaluated on real datasets and it is shown to achieve desirable properties with low computation and communication overhead for large-scale data markets.
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
1. The document discusses a new approach called the Cloud Analytics Reference Architecture that aims to better utilize big data.
2. It removes traditional constraints of data silos by consolidating all data in a "data lake" accessible for analysis.
3. This allows analysts to search for insights and patterns across all available data rather than being limited to specific predefined queries of individual data sets.
Data centric business and knowledge graph trendsAlan Morrison
The document discusses data-centric architecture and knowledge graphs. It defines key terms like data, content, and knowledge graphs. It discusses how knowledge graphs are evolving to be multi-model and can combine different data structures. The document argues that a data-centric approach is needed to reduce data and application silos and enable greater data reuse. It provides examples of how knowledge graphs can help industries like banking, pharmaceuticals, and oil and gas better manage their data assets and digital twins. The market potential for knowledge graph technologies is large but there is still low awareness of how they can help organizations.
As we enter the digital economy, it becomes increasingly transparent that the information and data ecosphere will continue to be a complex environment for the foreseeable future, with information being provided from a variety of internal and external sources in the form of files, messages, queries and streams. It would be foolish for any organization to place their bets on any one platform to be their platform of choice because it is incongruent to the thought patterns of the consumers, suppliers, regulators, partners and financiers who will participate in their information ecosphere through data feeds, information requests and a host of other interfaces.
Rather, there is a role of each of these platforms which serve as the conduit for data and the transformation of data into information aligned with the value propositions of the organization. This writing is focused on the big data platform because there are some unique characteristics of the big data environment that require an approach different than many of the legacy environments that exist in organizations. Furthermore, while big data is the one environment that is new and requires these special handling characteristics, there will be future platforms with the same requirements as big data requires today, and hopefully lessons learned will be left to not revisit each of the challenges as the next transformational information ecosphere is made available.
Figure 1 The Fourth Industrial Revolution, World Economic Forum, InfoSight Partners, 2016
This time is different, in that information is the catalyst to achieving value and the platform ideally suited to house information not optimal for storage in the form of rows and columns is the big data environment. Understanding which information is delivered with intended consequences and having the management prowess to tune information shared with customers, prospects, suppliers, partners, regulators and financiers is critical for the digital economy. Additionally, it is specific to understand the challenges each platform housing information bring to the equation. This writing will focus on big data.
This document discusses developing an effective information architecture (IA). It explains that an IA comes from understanding business objectives, constraints, content, and user requirements. The key steps in developing an IA are: understanding the business context, conducting content analysis, user research like card sorting, developing and evaluating a draft IA, documenting the final IA in a site map. An effective IA must reflect how users think about the content and support common tasks. The outputs of developing an IA include site maps, annotated page layouts, content matrices, and prototypes.
Impact of semantic technologies on scholarly publishingMills Davis
Semantic technologies will impact future business models for scholarly publishing.
First stage was the transition from publishing based on analog artifacts, to processes built for digital documents where computers are used as electronic pencils and XML based indices.
Second stage is semantic metadata where the computer is used to describe the published content in multiple ways -- think of it as a cambrian explosion of post-it notes -- and also the description and linking together of previously disparate sources. Data and content archives move beyond XML to description logic based semantic web standards which facilitate connect across media formats, documents, domains, and across archives leading to the need for community curation. Business models are still uncertain, being based on access and delivery of content for which alternatives are economically attractive.
Third stage is publishing based on (executable) knowledge-as-a-service. More than documents, more than passive semantic description, knowledge that is expressed through content, methods, data, and processes becomes modeled, managed, and enmeshed with research processes and processes which use the results of research. In this era, publishers with dominant positions in theory will find viable business models that trump competitors.
Decision Support Systems (DSS) are evolving to become Learning Support Systems (LSS), in this presentation I share my views on this topic and an approach on how the current DSS can be re-purposed by implementing a technology-agnostic, yet adaptable Knowledge Engineering Value Chain (KEVC).
The boom in Xaas and the knowledge graphAlan Morrison
The document discusses the growing importance of digital twins, knowledge graphs, and data-centric approaches to managing large, diverse datasets. It notes that current methods often struggle to integrate and contextualize data at scale. Effective digital twins and AI require integrated, disambiguated data flowing to where it's needed. Knowledge graphs are presented as a way to achieve this by providing a unified semantic model that treats relationships as a first-class citizen. The document outlines the large and growing markets for knowledge graph technologies and discusses how a data-centric approach can help enterprises better leverage emerging technologies.
The document discusses artificial intelligence (AI) and its potential applications for records management and compliance. It provides definitions of key AI concepts like machine learning, deep learning, and neural networks. It also discusses how AI can be used to automatically classify large amounts of unstructured information across different content repositories through techniques like supervised and unsupervised learning. The document suggests AI could help tackle issues like "information chaos" by enabling more effective compliance, records management, analytics and archiving of unstructured content.
The document discusses modernizing content management with Microsoft Content Services. It describes how traditional enterprise content management (ECM) systems focused on archiving and storage, whereas content services support broader business functions like collaboration. Content services provide a more dynamic lifecycle for content creation, coordination, protection and reuse. Microsoft and its partner HELUX provide tools like Microsoft Search, SharePoint, OneDrive and Azure to help organizations manage increasing volumes of content and meet compliance requirements.
SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITYAmit Sheth
Amit Sheth, SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITY, Keynote at:
CONTENT- AND SEMANTIC-BASED INFORMATION RETRIEVAL @ SCI 2002.
Scaling the mirrorworld with knowledge graphsAlan Morrison
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
This document is the table of contents for an MBA dissertation analyzing cloud computing in relation to its value and security/risk management. The dissertation will examine the benefits of cloud computing and identify associated risks. It will also explore security and risk management issues for companies adopting cloud computing and how to mitigate these risks. The dissertation will include a literature review on cloud computing's value and security challenges, a description of the data collection and analysis methodologies used, an analysis of survey results on industry professionals' views of cloud computing, and conclusions/recommendations.
WEB EVOLUTION - THE SHIFT FROM INFORMATION PUBLISHING TO REASONINGijaia
The Web, as communication channel, has had variety of development that allows information to be published and accessed in a scaleable approach. With the revolution of the information, some research studies have conducted to boost the present situation and propose advance version of the Web. Therefore, it is important to look into the new version of the Web in order to improve the way that information is expressed, to make more intelligent choices and to obtain a better meaning of the information over the Web. That is, future web would require specific architecture in order to support the extracting of better
meaning or "reasoning". With Web 1.0 and Web 2.0, the current information over the Web is not understandable for the machines. Understanding is big shift for wide open door for innovatoion and reasoning. In this work, we research the progress of the Web from Web 1.0, Web 2.0, Web 3.0, Web 4.0, to Web 5.0. We are pointing out document types and technologies employed to understand the changes from
Web 1.0 to Web 3.0 and to predicate the future of the Web (Web 4.0 and Web 5.0). Also, we present the current status and concerns about the Web as an information source and communication channel.
Three Dimensional Database: Artificial Intelligence to eCommerce Web service ...CSCJournals
A main objective of this paper is using artificial intelligence technique to web service agents and increase the efficiency of the agent communications. In recent years, web services have played a major role in computer applications. Web services are essential, as the design model of applications are dedicated to electronic businesses. This model aims to become one of the major formalisms for the design of distributed and cooperative applications in an open environment (the Internet). Current commercial and research-based efforts are reviewed and positioned within these two fields. A web service as a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-process able format (specifically Web Services Description Language WSDL). Other systems interact with the web service in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards. Particular attention is given to the application of AI techniques to the important issue of WS composition. Within the range of AI technologies considered, we focus on the work of the Semantic Web and Agent-based communities to provide web services with semantic descriptions and intelligent behavior and reasoning capabilities. Re-composition of web services is also considered and a number of adaptive agent approaches are introduced and implemented in publication domain with three dimensional databases and one of the areas of work is eCommerce.
The Next Step For Aritificial Intelligence in Financial ServicesAccenture Insurance
As financial services firms strive to transform their businesses for a digital world, realize efficiencies, improve the customer experience and revitalize their growth, they increasingly see artificial intelligence-based (AI) technologies as key. For firms, the next wave of AI innovation are artificial neural networks.
Don't Let Your Data Get SMACked: Introducing 3-D Data ManagementCognizant
Establishing data accuracy and quality is central to data management, but the SMAC stack - social, mobile, analytics and cloud - both makes it more complex to do so and offers tools for accomplishing the mission. We devised a three-tier "3-D" plan for data management based on integration, data fidelity and data integration.
Key Factors to Consider When Hosting DAM in the CloudCognizant
Detailed evaluation of various digital asset management (DAM) hosting service models - such as infrastructure as a service (IaaS) and software as a service (SaaS) - including costs, advantages and disadvantages, a set of prospective questions for DAM vendors and a use case example
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
1. The document discusses a new approach called the Cloud Analytics Reference Architecture that aims to better utilize big data.
2. It removes traditional constraints of data silos by consolidating all data in a "data lake" accessible for analysis.
3. This allows analysts to search for insights and patterns across all available data rather than being limited to specific predefined queries of individual data sets.
Data centric business and knowledge graph trendsAlan Morrison
The document discusses data-centric architecture and knowledge graphs. It defines key terms like data, content, and knowledge graphs. It discusses how knowledge graphs are evolving to be multi-model and can combine different data structures. The document argues that a data-centric approach is needed to reduce data and application silos and enable greater data reuse. It provides examples of how knowledge graphs can help industries like banking, pharmaceuticals, and oil and gas better manage their data assets and digital twins. The market potential for knowledge graph technologies is large but there is still low awareness of how they can help organizations.
As we enter the digital economy, it becomes increasingly transparent that the information and data ecosphere will continue to be a complex environment for the foreseeable future, with information being provided from a variety of internal and external sources in the form of files, messages, queries and streams. It would be foolish for any organization to place their bets on any one platform to be their platform of choice because it is incongruent to the thought patterns of the consumers, suppliers, regulators, partners and financiers who will participate in their information ecosphere through data feeds, information requests and a host of other interfaces.
Rather, there is a role of each of these platforms which serve as the conduit for data and the transformation of data into information aligned with the value propositions of the organization. This writing is focused on the big data platform because there are some unique characteristics of the big data environment that require an approach different than many of the legacy environments that exist in organizations. Furthermore, while big data is the one environment that is new and requires these special handling characteristics, there will be future platforms with the same requirements as big data requires today, and hopefully lessons learned will be left to not revisit each of the challenges as the next transformational information ecosphere is made available.
Figure 1 The Fourth Industrial Revolution, World Economic Forum, InfoSight Partners, 2016
This time is different, in that information is the catalyst to achieving value and the platform ideally suited to house information not optimal for storage in the form of rows and columns is the big data environment. Understanding which information is delivered with intended consequences and having the management prowess to tune information shared with customers, prospects, suppliers, partners, regulators and financiers is critical for the digital economy. Additionally, it is specific to understand the challenges each platform housing information bring to the equation. This writing will focus on big data.
This document discusses developing an effective information architecture (IA). It explains that an IA comes from understanding business objectives, constraints, content, and user requirements. The key steps in developing an IA are: understanding the business context, conducting content analysis, user research like card sorting, developing and evaluating a draft IA, documenting the final IA in a site map. An effective IA must reflect how users think about the content and support common tasks. The outputs of developing an IA include site maps, annotated page layouts, content matrices, and prototypes.
Impact of semantic technologies on scholarly publishingMills Davis
Semantic technologies will impact future business models for scholarly publishing.
First stage was the transition from publishing based on analog artifacts, to processes built for digital documents where computers are used as electronic pencils and XML based indices.
Second stage is semantic metadata where the computer is used to describe the published content in multiple ways -- think of it as a cambrian explosion of post-it notes -- and also the description and linking together of previously disparate sources. Data and content archives move beyond XML to description logic based semantic web standards which facilitate connect across media formats, documents, domains, and across archives leading to the need for community curation. Business models are still uncertain, being based on access and delivery of content for which alternatives are economically attractive.
Third stage is publishing based on (executable) knowledge-as-a-service. More than documents, more than passive semantic description, knowledge that is expressed through content, methods, data, and processes becomes modeled, managed, and enmeshed with research processes and processes which use the results of research. In this era, publishers with dominant positions in theory will find viable business models that trump competitors.
Decision Support Systems (DSS) are evolving to become Learning Support Systems (LSS), in this presentation I share my views on this topic and an approach on how the current DSS can be re-purposed by implementing a technology-agnostic, yet adaptable Knowledge Engineering Value Chain (KEVC).
The boom in Xaas and the knowledge graphAlan Morrison
The document discusses the growing importance of digital twins, knowledge graphs, and data-centric approaches to managing large, diverse datasets. It notes that current methods often struggle to integrate and contextualize data at scale. Effective digital twins and AI require integrated, disambiguated data flowing to where it's needed. Knowledge graphs are presented as a way to achieve this by providing a unified semantic model that treats relationships as a first-class citizen. The document outlines the large and growing markets for knowledge graph technologies and discusses how a data-centric approach can help enterprises better leverage emerging technologies.
The document discusses artificial intelligence (AI) and its potential applications for records management and compliance. It provides definitions of key AI concepts like machine learning, deep learning, and neural networks. It also discusses how AI can be used to automatically classify large amounts of unstructured information across different content repositories through techniques like supervised and unsupervised learning. The document suggests AI could help tackle issues like "information chaos" by enabling more effective compliance, records management, analytics and archiving of unstructured content.
The document discusses modernizing content management with Microsoft Content Services. It describes how traditional enterprise content management (ECM) systems focused on archiving and storage, whereas content services support broader business functions like collaboration. Content services provide a more dynamic lifecycle for content creation, coordination, protection and reuse. Microsoft and its partner HELUX provide tools like Microsoft Search, SharePoint, OneDrive and Azure to help organizations manage increasing volumes of content and meet compliance requirements.
SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITYAmit Sheth
Amit Sheth, SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITY, Keynote at:
CONTENT- AND SEMANTIC-BASED INFORMATION RETRIEVAL @ SCI 2002.
Scaling the mirrorworld with knowledge graphsAlan Morrison
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
This document is the table of contents for an MBA dissertation analyzing cloud computing in relation to its value and security/risk management. The dissertation will examine the benefits of cloud computing and identify associated risks. It will also explore security and risk management issues for companies adopting cloud computing and how to mitigate these risks. The dissertation will include a literature review on cloud computing's value and security challenges, a description of the data collection and analysis methodologies used, an analysis of survey results on industry professionals' views of cloud computing, and conclusions/recommendations.
WEB EVOLUTION - THE SHIFT FROM INFORMATION PUBLISHING TO REASONINGijaia
The Web, as communication channel, has had variety of development that allows information to be published and accessed in a scaleable approach. With the revolution of the information, some research studies have conducted to boost the present situation and propose advance version of the Web. Therefore, it is important to look into the new version of the Web in order to improve the way that information is expressed, to make more intelligent choices and to obtain a better meaning of the information over the Web. That is, future web would require specific architecture in order to support the extracting of better
meaning or "reasoning". With Web 1.0 and Web 2.0, the current information over the Web is not understandable for the machines. Understanding is big shift for wide open door for innovatoion and reasoning. In this work, we research the progress of the Web from Web 1.0, Web 2.0, Web 3.0, Web 4.0, to Web 5.0. We are pointing out document types and technologies employed to understand the changes from
Web 1.0 to Web 3.0 and to predicate the future of the Web (Web 4.0 and Web 5.0). Also, we present the current status and concerns about the Web as an information source and communication channel.
Three Dimensional Database: Artificial Intelligence to eCommerce Web service ...CSCJournals
A main objective of this paper is using artificial intelligence technique to web service agents and increase the efficiency of the agent communications. In recent years, web services have played a major role in computer applications. Web services are essential, as the design model of applications are dedicated to electronic businesses. This model aims to become one of the major formalisms for the design of distributed and cooperative applications in an open environment (the Internet). Current commercial and research-based efforts are reviewed and positioned within these two fields. A web service as a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-process able format (specifically Web Services Description Language WSDL). Other systems interact with the web service in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards. Particular attention is given to the application of AI techniques to the important issue of WS composition. Within the range of AI technologies considered, we focus on the work of the Semantic Web and Agent-based communities to provide web services with semantic descriptions and intelligent behavior and reasoning capabilities. Re-composition of web services is also considered and a number of adaptive agent approaches are introduced and implemented in publication domain with three dimensional databases and one of the areas of work is eCommerce.
The Next Step For Aritificial Intelligence in Financial ServicesAccenture Insurance
As financial services firms strive to transform their businesses for a digital world, realize efficiencies, improve the customer experience and revitalize their growth, they increasingly see artificial intelligence-based (AI) technologies as key. For firms, the next wave of AI innovation are artificial neural networks.
Don't Let Your Data Get SMACked: Introducing 3-D Data ManagementCognizant
Establishing data accuracy and quality is central to data management, but the SMAC stack - social, mobile, analytics and cloud - both makes it more complex to do so and offers tools for accomplishing the mission. We devised a three-tier "3-D" plan for data management based on integration, data fidelity and data integration.
Key Factors to Consider When Hosting DAM in the CloudCognizant
Detailed evaluation of various digital asset management (DAM) hosting service models - such as infrastructure as a service (IaaS) and software as a service (SaaS) - including costs, advantages and disadvantages, a set of prospective questions for DAM vendors and a use case example
Emerging Differentiators of a Successful Wealtlh Management PlatformCognizant
Changes in the wealth management industry are driving the need for a flexible, scalable platform that enables wealth managers to differentiate their services and profitably serve the mass affluent and mass markets.
Selecting a Software Solution: 13 Best Practices for Media and Entertainment ...Cognizant
When selecting commercial off-the-shelf software (COTS), companies in the increasingly digitally-based media and entertainment industry need to develop a detailed advance plan, obtain support from all stakeholders and continuously monitor vendor performance against critical expectations, best practices and business requirements.
Using Gamification to Build a Passionate and Quality-Driven Software Developm...Cognizant
Gamification techniques are increasingly playing a huge role in software development - to motivate team members, reduce the cost of quality, reward high achievers and more. We suggest you begin software gamifying with project management, innovation, the software-development training process and delivery.
The Attorney Scorecard: Accelerating the Foreclosure Process while Improving ...Cognizant
This document discusses implementing an attorney scorecard system to improve the mortgage foreclosure process. It notes that foreclosure is a lengthy and expensive legal process, and that introducing a scorecard can help servicers more closely monitor attorney performance, balance workloads, and improve compliance and efficiency. The scorecard would track key performance indicators and milestones to evaluate attorneys and identify areas for improvement. Implementing such a system faces challenges around integrating data from different systems and standardizing metrics, but could help speed up the foreclosure timeline and reduce costs.
U.S. Travel and Hospitality: Embracing a SMAC-Driven FutureCognizant
Airlines, hotels and travel agents are driven to holistically embrace social, mobile, advanced analytics and cloud to boost business performance and retain share of wallet.
By focusing on organizational enablers and robust software engineering practices, e-commerce companies can shorten the development lifecycle, outmaneuver the competition and remain relevant in the eyes of customers.
The World Wide Web is booming and radically vibrant due to the well established standards and widely accountable framework which guarantees the interoperability at various levels of the application and the society as a whole. So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for automatic processing and machine-level understanding. The well set notion, Semantic Web would be turn possible if only there exists the further levels of interoperability prevails among the applications and networks. In achieving this interoperability and greater functionality among the applications, the W3C standardization has already released the well defined standards such as RDF/RDF Schema and OWL. Using XML as a tool for semantic interoperability has not achieved anything effective and failed to bring the interconnection at the larger level. This leads to the further inclusion of inference layer at the top of the web architecture and its paves the way for proposing the common design for encoding the ontology representation languages in the data models such as RDF/RDFS. In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in the web.
This document provides summaries of trends in IT, including cloud computing, business analytics, artificial intelligence and machine learning, and database management systems. It discusses how cloud computing allows users to access computing resources over the internet rather than owning hardware. It also explains how business analytics uses data and modeling to help businesses make decisions, and how artificial intelligence and machine learning use algorithms to enable machines to learn from data and mimic human behavior. Finally, it defines a database management system as software that interfaces with databases and allows users to organize, access, and manage data.
Week 4 Lecture 1 - Databases and Data WarehousesManagement of .docxjessiehampson
Week 4 Lecture 1 - Databases and Data Warehouses
Management of Information Systems
Databases and Data Warehouses
The impact of database technology on how business is conducted today cannot be overemphasized. This technology has enabled an information industry with comprehensive influences on businesses and individuals. Databases store data that populate web pages and other interactive networked technologies. Search engines, e-commerce, and social media would not exist without databases. With database support, larger tasks can be accomplished by fewer people.
Effective data management is the principal benefit of IT. Database management systems (DBMSs) enable the fast creation of databases and manipulation of data on an aggregate basis or down to the smallest detail for business purposes. Databases support most web pages and other interactive networked technology. DBMSs support target marketing, financial management, decision-making, distribution of goods and services, customer service, and other activities. It is imperative, in the age of data mining, and “big data,” for knowledge workers to understand how databases work and how data are used operationally and strategically in business management.
Database analysis and management skills are mandatory in the marketplace. IT professionals develop and implement databases. However, data is essential to the non-technical professional who uses the data for decision making regarding accounting, marketing, logistics, senior management, and other functional areas.
The relational database model is common. However, data can be organized in other ways. “Big Data” prompted the use of other database models. “NoSQL” database models are non-relational and do not require SQL to retrieve data. NoSQL databases can be structured by object, document, key-value, graph, column, and other possibilities
In relational databases, a primary key is a field in a table that contains a unique value used to differentiate between rows of data. The primary key is usually a number, or a computer generated globally unique identifier (GUID). Sometimes a composite key is used differentiate between table rows. A composite key is a combination of the values in two or more fields in a table that when combined are unique in the table and serve as a primary key. A foreign key is used to link data between two tables. A foreign key in a table is the primary key of a related table.
Databases contain different types of fields. Some types are, number, text, image, video, audio, geographical coordinates, and others. If a number is not used for mathematical calculations, it is best to assign a text type to it in a database to avoid the need to convert it from a number to a string after retrieval.
SQL is a popular query language used to retrieve data from relational databases. SQL can be used to retrieve data from more than one table by use of a “join.” A join query retrieves data from rows in two or more tables, where the value of the foreign ...
Improve information retrieval and e learning usingIJwest
The Web-based education and E-Learning has become a very important branch of new educational technology. E-learning and Web-based courses offer advantages for learners by making access to resources and learning objects very fast, just-in-time and relevance, at any time or place. Web based Learning Management Systems should focus on how to satisfy the e-learners needs and it may advise a learner with most suitable resources and learning objects. But Because of many limitations using web 2.0 for creating E-learning management system, now-a-days we use Web 3.0 which is known as Semantic web. It is a platform to represent E-learning management system that recovers the limitations of Web 2.0.In this paper we present “improve information retrieval and e-learning using mobile agent based on semantic web technology”. This paper focuses on design and implementation of knowledge-based industrial reusable, interactive, web-based training activities at the sea ports and logistics sector and use e-learning system and semantic web to deliver the learning objects to learners in an interactive, adaptive and flexible manner. We use semantic web and mobile agent to improve Library and courses Search. The architecture presented in this paper is considered an adaptation model that converts from syntactic search to semantic search. We apply the training at Damietta port in Egypt as a real-world case study. we present one of possible applications of mobile agent technology based on semantic web to management of Web Services, this model improve the information retrieval and E-learning system.
Real Semantics is a product designed with BCBS 239 compliance in mind. It uses a universal graph model and common data model to trace decisions made by systems. It can peer into legacy systems at different levels. This synchronization of data takes chaos out of IT systems. Real Semantics satisfies BCBS 239 requirements such as establishing integrated data taxonomies, ownership and quality of risk data, and capabilities to generate risk data subsets quickly. While many organizations struggle with regulations, Real Semantics sees it as an opportunity to improve systems to satisfy customers and grow business.
The document discusses how the rise of web-oriented architecture is changing enterprise IT by enabling companies to build a "social layer" in their architecture. Key points:
- Dynamic scripting languages, open frameworks, and the "view source" culture of the open web have made software development more accessible and disrupted traditional enterprise languages and frameworks.
- Representational State Transfer (REST) APIs and adherence to HTTP have dramatically lowered barriers to integrating applications and allowed the success seen on consumer web services to migrate into enterprises.
- Companies are now embracing social software to break down information silos between people and systems. A social layer surfaces information from various applications into social platforms for collaboration.
How Structured data benefits search engines and user experiencetechcraftpranto
structured data plays an essential role in web development; through markup languages like HTML or schema.org annotations, search engines can better understand and display web content in rich snippets or other enhanced formats – improving user experience and potentially enhancing SEO rankings.
Semantic Technology. Origins and Modern Enterprise Usemyankova
With the help of Semantic Technology rather than locked into siloed, proprietary data formats that impede storage, access and retrieval, data pieces would seamlessly become interoperable and easy to integrate.
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...IRJET Journal
This document discusses big data analytical methods, cloud computing, and how they can be combined. It explains that big data involves large amounts of structured, semi-structured, and unstructured data from various sources that requires significant computing resources to analyze. Cloud computing provides a way for big data analytics to be offered as a service and processed efficiently using cloud resources. The integration of big data and cloud computing allows organizations to gain business intelligence from large datasets in a flexible, scalable and cost-effective manner.
This document discusses how new technologies like the Internet of Things, big data, and smart city initiatives are challenging existing sectors like content management to integrate these new technologies and data streams. Specifically, IoT and sensors are producing vast amounts of real-time data that needs to be connected and accessible across different systems. Content management must adapt to integrate this new data in automated and traceable ways. Technologies like big data analytics and geomatics also impact content management by enabling new forms of analysis and location-based information and services. The future requires increased innovation to allow for seamless integration of these new technologies and data sources.
Real time responses to events will be feasible when enterprises are designed to be maneuverable and their flow of activity is not disrupted by a breakdown in any one component in the chain of business processes that enable the completion of an activity.
Next generation semantic technologies help individuals and businesses develop semantic superpowers. That is how we break the chains of legacy systems, free resources from maintenance, and innovate the new capabilities we need to thrive in the next Internet. Learn what semantic superpowers can do for you.
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
Business software and information technology are changing rapidly, and so is the terminology used by business professionals, software and IT professionals. For your reference, following is an alphabetical listing of terms that will be updated frequently for accuracy. Have a suggestion for a term?
This document discusses enabling analytics as a service (AaaS) on IBM SoftLayer Cloud. It describes how various analytical platforms and workloads have been modernized, migrated, and deployed on the SoftLayer Cloud to provide analytics capabilities as a service. Specifically, it outlines big data analytics platforms like Cloudera, Hortonworks, MapR, and IBM BigInsights that have been implemented on the cloud. It also discusses real-time analytics platforms like VoltDB and Apache Storm that have been deployed on SoftLayer Cloud to enable real-time analytics and processing of fast data streams.
First Firecat Friday presentation: tools, best practices and design insights we've put to work for organizations of all sizes to help groups and teams work on projects, share ideas, keep track of files, stay on top of tasks -- while feeling like a team.
The document discusses SMAC (social, mobile, analytics, and cloud) technologies and how they are integrated to transform enterprises. It provides examples of how some state governments in India are implementing SMAC technologies for e-governance initiatives. When combined as a stack, SMAC allows organizations to become more connected, collaborative, and productive. Major IT companies are helping implement SMAC solutions across various applications like mobile banking, e-governance, marketing, and more. While SMAC provides advantages like improved partnerships, some risks and challenges around data security and privacy also exist.
Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Pr...Cognizant
Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.
Data Modernization: Breaking the AI Vicious Cycle for Superior Decision-makingCognizant
The document discusses how most companies are not fully leveraging artificial intelligence (AI) and data for decision-making. It finds that only 20% of companies are "leaders" in using AI for decisions, while the remaining 80% are stuck in a "vicious cycle" of not understanding AI's potential, having low trust in AI, and limited adoption. Leaders use more sophisticated verification of AI decisions and a wider range of AI technologies beyond chatbots. The document provides recommendations for breaking the vicious cycle, including appointing AI champions, starting with specific high-impact decisions, and institutionalizing continuous learning about AI advances.
It Takes an Ecosystem: How Technology Companies Deliver Exceptional ExperiencesCognizant
Experience is becoming a key strategy for technology companies as they shift to cloud-based subscription models. This requires building an "experience ecosystem" that breaks down silos and involves partners. Building such an ecosystem involves adopting a cross-functional approach to experience, making experience data-driven to generate insights, and creating platforms to enable connected selling between companies and partners.
Intuition is not a mystery but rather a mechanistic process based on accumulated experience. Leading businesses are engineering intuition into their organizations by harnessing machine learning software, massive cloud processing power, huge amounts of data, and design thinking in experiences. This allows them to anticipate and act with speed and insight, improving decision making through data-driven insights and acting as if on intuition.
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...Cognizant
The T&L industry appears poised to accelerate its long-overdue modernization drive, as the pandemic spurs an increased need for agility and resilience, according to our study.
Enhancing Desirability: Five Considerations for Winning Digital InitiativesCognizant
To be a modern digital business in the post-COVID era, organizations must be fanatical about the experiences they deliver to an increasingly savvy and expectant user community. Getting there requires a mastery of human-design thinking, compelling user interface and interaction design, and a focus on functional and nonfunctional capabilities that drive business differentiation and results.
The Work Ahead in Manufacturing: Fulfilling the Agility MandateCognizant
Manufacturers are ahead of other industries in IoT deployments but lag in investments in analytics and AI needed to maximize IoT's benefits. While many have IoT pilots, few have implemented machine learning at scale to analyze sensor data and optimize processes. To fully digitize manufacturing, investments in automation, analytics, and AI must increase from the current 5.5% of revenue to over 11% to integrate IT, OT, and PT across the value chain.
The Work Ahead in Higher Education: Repaving the Road for the Employees of To...Cognizant
Higher-ed institutions expect pandemic-driven disruption to continue, especially as hyperconnectivity, analytics and AI drive personalized education models over the lifetime of the learner, according to our recent research.
Engineering the Next-Gen Digital Claims Organisation for Australian General I...Cognizant
The document discusses potential future states for the claims organization of Australian general insurers. It notes that gradual changes like increasing climate volatility, new technologies, and changing customer demographics will reshape the insurance industry and claims processes. Five potential end states for claims organizations are described: 1) traditional claims will demand faster processing; 2) a larger percentage of claims will come from new digital risks; 3) claims processes may become "Uberized" through partnerships; 4) claims organizations will face challenges in risk management propositions; 5) humans and machines will work together to adjudicate claims using large data and computing power. The document argues that insurers must transform claims through digital technologies to concurrently improve customer experience, operational effectiveness, and efficiencies
Profitability in the Direct-to-Consumer Marketplace: A Playbook for Media and...Cognizant
Amid constant change, industry leaders need an upgraded IT infrastructure capable of adapting to audience expectations while proactively anticipating ever-evolving business requirements.
Green Rush: The Economic Imperative for SustainabilityCognizant
Green business is good business, according to our recent research, whether for companies monetizing tech tools used for sustainability or for those that see the impact of these initiatives on business goals.
Policy Administration Modernization: Four Paths for InsurersCognizant
The pivot to digital is fraught with numerous obstacles but with proper planning and execution, legacy carriers can update their core systems and keep pace with the competition, while proactively addressing customer needs.
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
Utilities are starting to adopt digital technologies to eliminate slow processes, elevate customer experience and boost sustainability, according to our recent study.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Operations Workforce Management: A Data-Informed, Digital-First ApproachCognizant
As #WorkFromAnywhere becomes the rule rather than the exception, organizations face an important question: How can they increase their digital quotient to engage and enable a remote operations workforce to work collaboratively to deliver onclient requirements and contractual commitments?
Five Priorities for Quality Engineering When Taking Banking to the CloudCognizant
As banks move to cloud-based banking platforms for lower costs and greater agility, they must seamlessly integrate technologies and workflows while ensuring security, performance and an enhanced user experience. Here are five ways cloud-focused quality assurance helps banks maximize the benefits.
Getting Ahead With AI: How APAC Companies Replicate Success by Remaining FocusedCognizant
Changing market dynamics are propelling Asia-Pacific businesses to take a highly disciplined and focused approach to ensuring that their AI initiatives rapidly scale and quickly generate heightened business impact.
The Work Ahead in Intelligent Automation: Coping with Complexity in a Post-Pa...Cognizant
Intelligent automation continues to be a top driver of the future of work, according to our recent study. To reap the full advantages, businesses need to move from isolated to widespread deployment.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Semantic Enterprise: A Step Toward Agent-Driven Integration
1. Semantic Enterprise: A Step Toward
Agent-Driven Integration
Knowledge-driven enterprises can become more adaptable, dynamic
and collaborative by using semantic technologies to integrate openly
available data into the ecosystem.
Executive Summary
Technology waves are rolling in faster than ever,
and enterprises are being reshaped by emerging
Web technologies. Across industries, technology
is no longer just a support system but also an
integral part of today’s fast-moving, “learn as you
go” business models.
For instance, beyond historical transactional data,
businesses are looking for more current and
up-to-date data for informed decision-making.
Retailers are integrating external blogs, social
sites and data from ubiquitous mobile devices
into their analytics systems to better understand
the market pulse. Insurance companies, agents
and customers are collaborating more effectively
by integrating their systems to work online.
But the job of incorporating external data or
onboarding external agents today often involves
manual processes and development cycles that
span months. With the advancement of semantic
technologies, however, businesses can integrate
openly available data into the ecosystem more
effectively.
In this white paper, we discuss semantic technol-
ogy adoption and how these capabilities are reori-
enting the enterprise. We look at how semantic
technologies can enable more agile and automat-
ed approaches to integration, and we highlight
some of the challenges associated with semantic
technology adoption.
Agents and Semantics
To understand how business is evolving, it is
important to track the evolution of Web tech-
nologies, as these are currently a key business
enabler. Even before the social Web (or Web 2.0)
was fully realized, business leaders began leverag-
ing emerging semantic technologies (Web 3.0) to
enable data sharing among social networks, with
user permission. Then, just as Web 3.0 was learn-
ing to walk, Web 4.0, the self-learning and self-
organizing Web, began incubating. Figure 1, next
page, describes these technology waves in detail.
To understand the semantic Web, it’s important to
first grasp two core concepts: semantic technolo-
gies and agents.
• Semantic technologies: Data is meaning-
ful only when it is accompanied by structure.
While syntax is about grammatical structure,
semantics governs the relationship between
articles, vowels, consonants, verbs and associat-
• Cognizant 20-20 Insights
cognizant 20-20 insights | december 2013
2. 2
ed rules. Both the syntax and the semantics of a
domain need to be defined in machine language
so that the machine or computer systems can
interpret that domain. An example is the spell
checker, a software agent that compiles work
based on lexical syntax and semantics, artic-
ulated in machine language. Similarly, it is
important to define the semantics of a business
domain or enterprise so that the IT systems can
operate more efficiently.
Semantic technologies aim to create a
knowledge base for computer programs to
work more intelligently by linking objects
and building relationships. The knowledge
base helps computer systems understand
the context of the work being accomplished
during runtime operations and anticipate the
necessary actions, with the goal of building
relationships between every object in the world
and creating a web of data. (For more on this
topic, see our white paper, “How Semantic
Technology Drives Agile Business.”)
• Agents: Agents are complex software systems
that are designed to perform a variety of
tasks by interpreting a machine-readable
knowledge base. For example compilers are
software agents that work based on the
syntax and semantics defined for a program-
ming language. Similarly, Web crawlers are
complex agents that retrieve processes and
harvest data automatically from Web sites. A
smarter breed of Web crawlers is emerging for
harvesting semantic Web content.
Figure 2 illustrates how a computer system can
use data to develop wisdom by adding structure
and meaning to data and processing it.
cognizant 20-20 insights
Figure 1
Evolution of the Web
Technology
Wave
Focal
Point
Characteristics Key Enablers
Approximate
Timeline
Web 1.0
Information
Web
Rudimentary personal and
corporate Web sites
HTML, directories,
data silos
1994-2000
Web 2.0 Social Web
Personal blogging, social media
and networking
CSS, P2P, AJAX 2000-2010
Web 3.0 Semantic Web
Distributed social Web, with open
data sharing (with permission)
SPARQL, OWL,
semantic search
2010-2020
Web 4.0
Intelligent
Web
Humans and computer systems
interacting in
information symbiosis
Intelligent agents,
distributed search,
ubiquity
2020-2030
Figure 2
How Data Leads to Wisdom
Information
Analytics/AI/Data Mining
Add Meaning
Add Structure
Num
mbers
s, te
ex
xt, images
s, videos, etc
c.
Wisdom
Knowledge
Data
A
A U.S
S. citi
izen is
s likely
y to have a
a Social Security Number.
Resid
dent
ts o
of a co
ount
try a e a p
personal identifier;
a
a Soc
cial Se
e
ecurity N
Numb r is a p
personal identifier.
S
Socia
al S
Secu
urit Nu
umb
ber =
123
3-45
5-67
789
9. Bu
uilding plan = xyz
z.jpg
g
S
St
tr
ru
uc
c
ma
age
M
Mea
s/
/A
AI
I/
/D
Dat
v
ve a
ha
ave
b
be
e
e
er is
r
ri
it
ty
y N
uild
ding
g
3. 3
cognizant 20-20 insights
Semantic Web
The semantic Web is about making it easier
for computer systems to interpret content. Its
primary focus is tagging the content based on
what it “means,” thus adding structure to data.
The semantic Web is an indirect response to
the business need for efficiency and getting the
most out of its investments, from employees to
equipment.
For example, in 2010, the BBC upgraded its
World Cup Web site with semantic Web technolo-
gies, curating and interlinking the site’s content,
without employing a large fleet of editors.1
The
result: a highly dynamic, interactive, information-
rich and user-oriented site, with aggregation at
many levels (such as player, team, geography and
group).
The richness of the site’s information, which
included 700 topical index pages, could never
have been produced via traditional methods.
Semantic Web technologies, on the other hand,
added structure to the unstructured information,
typically handled by media, through appropriate
tagging.
Another key aspect of the Web site’s success was
cross-document relationships; ontology helped
capture the complex interlinking of the documents
based on topics, authors, citations and multiple
revisions. Managing these relationships through
traditional relational databases would have been
cumbersome and inefficient, increasing time to
market. Semantic Web databases — generally
known as knowledge bases — can store more
sophisticated and referenceable metadata than
relational databases. Thus, they allow complex
algorithms to directly reason with inferences on
the data structures.
The interlinked, metadata-driven nature of the
semantic Web enables enterprises to stay abreast
of constantly changing usage patterns. The
standardized metadata helps computer systems
decipher meaning and act on it. Agents, thus, can
run complex algorithms to directly reason with
inferences on the data structures. This is why
semantic Web languages are a key part of the
knowledge representation of artificial intelligence
(AI).
Other organizations have created production
systems with semantic Web technologies, as well,
including Time, Inc., Elsevier and the Library of
Congress.2
Semantic Enterprise
The semantic, or knowledge-driven, enterprise
describes content using ontologies by tagging
and linking information. This results in an inter-
linked, rich information tree of knowledge that
continues to grow over time. The semantic enter-
prise provides contextual connections to both
the identity of the enterprise and the assets that
keep it running, creating a
knowledgebase for computer
systems to interpret the
meaning of their actions. This
results in more efficient pro-
cessing and decision-making.
Assets are defined by the
people, process and technol-
ogy resources associated with
the enterprise, and identity is
defined by capturing the enter-
prise vision, mission, strategy
and principles.
The Department of Defense
(DoD) makes use of semantic
technology across systems to
form an executable, integrated
and consumable architecture.3
Since 2011, the DoD’s Business
Mission Area has mandated
the use of semantic Web technologies as the
foundational architecture for new integration
projects. The organization links disparate infor-
mation systems by overlaying them with semantic
models, which has decreased the time it takes to
get a new enterprise system up and running from
six to nine months to less than 90 days.
Current tools enable enterprise modeling to
define the relationships among various enterprise
entities. When these models are maintained, the
semantic enterprise can distinguish the present
state from the past, add constraints, guide the
present and predict the future.
However, current enterprise architecture tools
are not flexible enough for extending the model
with respect to newer entities. For example, these
tools cannot be extended to capture physical
assets along with the technology assets. Because
these tools are not semantically aware, they
provide a model that can neither be interpreted
by other software nor be exchanged with the
extended enterprise or the external world. As
such, it becomes an uphill task for the enterprise
to adapt to the dynamics of change.
Semantic Web
databases —
generally known as
knowledgebases
— can store more
sophisticated and
referenceable
metadata than
relational databases.
Thus, they allow
complex algorithms
to directly reason
with inferences on
the data structures.
4. cognizant 20-20 insights 4
Semantic Technologies and
Agent-Driven Integration
With traditional business-to-business integra-
tion, partners end up making a series of changes
to their underlying systems in order to use each
other’s services. This is a long and drawn-out
process that involves risk, time and money. In
the end, even if the integration is successful, the
partnership that once seemed lucrative may no
longer be so due to the vital time lost in getting
the services up and running.
Imagine a situation where online retailers can
integrate services of new vendors in weeks if not
days. This is possible if a comprehensive, standard
semantic model is used that contains object defi-
nitions with create, read, update, delete and store
(CRUDS) operations that are linked to associated
services and data mapping details. In this case,
an integration agent can identify the required
service for fetching the set of attributes of a
specified object.
Figure 3 partially depicts an enterprise that is
semantically described by linking resources. A
semantic-aware agent program can be used to
integrate multiple enterprises or services within
an enterprise, provided the partner enterprise
extends the integration ontology that is under-
stood by the agent program.
In a hypothetical example of an online retailer’s
integration architecture, the business partners
describe their assets (such as watches, shoes
and perfumes) and services (such as search,
order and payment) by providing mapping or
extensions to the abstract product and service
ontology. The business partners register them-
selves with the retailer by extending an abstract
partner ontology.
When a consumer visits the portal that is provided
by the retailer to make a purchase, the agent
fulfills the request to list all the watches. The
agent does this by discovering all the business
partners that provide watches and deciding which
services need to be invoked to display the required
list, as well as the mediation needed for such an
invocation. The search request is executed after-
wards, and the result is rendered in the portal.
Thus, the rendering agent, being semantic-aware,
can seamlessly display products that contain the
required attributes, without any prior awareness
of them.
Disadvantages of Semantic
Technologies
The flexibility inherent in semantic Web applica-
tions introduces some drawbacks. Sometimes, a
problem can be more efficiently solved by using
other tools. The following are just a few char-
acteristics of applications that can hamper the
effectiveness of semantic Web tools:
• Data volume: The data volume stored in a
relational data warehouse cannot be handled
by a single semantic solution. A workaround
(such as a just-in-time data mart and query
Figure 3
Agent-Driven Enterprise Integration Ontology
Engine Enterprise
Integration
Enterprise
Asset
Process
Platform
Software Hardware
Service
Partner
Agent
Base
Ontology
Asset
Ontology
Application
Implements
Provides
v
Has
a
n
Is an
Understands
s
Extends
n
Discovers
Invokes
ok
Extends
e
e
Consists of
s
Consists of
Runs on
Runs on
Driven by
ve
E
Ext
tend
ds D
Descri
ib
bed
d b
by
Works with
s
Consists of
t
Consists Of
5. cognizant 20-20 insights 5
federation) may be employed to boost the
scale of effectiveness. However, doing so will
increase the complexity of the solution to a
great extent and may not be feasible in certain
cases.
• Update transaction volume: Semantic
solutions are not suitable for handling high-vol-
ume transactions, such as thousands of reads
or writes per second to a single server. These
solutions are especially not recommended for
high-volume writes.
• Computational scale: Present-day semantic
Web tools are not optimized for high-scale
numeric computations on a huge amount of
numeric data. Therefore, the recommended
workaround is to pull data from semantic Web
systems into traditional business intelligence
tools for calculation and visualization.
Barring these drawbacks, because semantic
Web solutions are storage-agnostic, a relation-
al database for the high-volume transactional
server can be wrapped within semantic query
endpoints in order to integrate it with a broader
semantic Web application or strategy. Similarly,
an existing data warehouse containing petabytes
of data can be consumed by semantic Web appli-
cations by defining ontologies for the subsets of
warehoused data that need to be consumed.
Looking Ahead
Relational systems are still superior for trans-
actional and operational systems. However,
they deal with certainty and logical objects. It is
difficult to extend relational systems to support
an uncertain real-world object network. Because
of this, they fall short in enabling knowledge
management, which is incomplete, changing and
uncertain. The semantic Web ensures that artifi-
cial thresholds are not placed on information, and
it encourages collaboration between enterprises.
As open standards evolve, semantic technolo-
gies will thrive. And as the business world adopts
semantic technologies, the semantic enterprise
will emerge.
References
• Jeffrey T. Pollock, Semantic Web For Dummies, Wiley, 2009.
• “Gartner Identifies Top Technology Trends Impacting Information Infrastructure in 2013,” Gartner,
Inc., March 6, 2013, http://www.gartner.com/newsroom/id/2359715.
• “Towards Executable Enterprise Models: Building Semantic Enterprise Architecture Solutions with
TopBraid Suite,” TopQuadrant, Inc., http://www.topquadrant.com/docs/whitepapers/WP-BuildingSe-
manticEASolutions-withTopBraid.pdf.
• “Semantic Web,” World Wide Web Consortium, http://www.w3.org/standards/semanticweb/.
• Steve Andriole, “Enterprise 3.0: How It’s All Going to Change,” Acentio,
http://www.acentio.com/downloads/Andriole_Enterprise-3.0-How-IT’s-All-Going-to-Change.pdf.
• James Hendler, “Agents and the Semantic Web,” IEEE Intelligent Systems Journal, March/April 2001,
http://www.cs.rpi.edu/~hendler/AgentWeb.html.
• http://900igr.net/prezentatsii/informatika/Informatsionno-kommunikatsionnye-tekhnologii-v-
obrazovanii/017-A.-Amman-T.Kiss.html.
Footnotes
1
John O’Donovan, “The World Cup and a Call to Action Around Linked Data,” BBC Internet Blog, July 9,
2010, http://www.bbc.co.uk/blogs/bbcinternet/2010/07/the_world_cup_and_a_call_to_ac.html.
2
“Example Semantic Web Applications,” Cambridge Semantics, http://www.cambridgesemantics.com/
semantic-university/example-semantic-web-applications.
3
“SemTechBiz Keynote: Department of Defense Mandates Use of Semantic Technology,” Semanticweb.
com, July 1, 2011, http://semanticweb.com/semtechbiz-keynote-department-of-defense-mandates-use-of-
semantic-technology/.