Xml Xslt
View more OpenOffice presentations from Domenico Briganti.
domingo, 7 de junho de 2009
XML Schema (W3C)
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XML Schema (W3C)
View more OpenOffice presentations from pohjus.
Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
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Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
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RDF and OWL
Check out this SlideShare Presentation:
RDF and OWL
View more OpenOffice presentations from Rachel Lovinger.
Ontologias - RDF
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Ontologias - RDF
View more Microsoft Word documents from adorepump.
sábado, 6 de junho de 2009
Legal Technology - Legal Ontologies Spin a Semantic Web
Legal Technology - Legal Ontologies Spin a Semantic Web: "Legal Ontologies Spin a Semantic Web
By Dr. Adam Z. Wyner
Special to Law.com
June 8, 2009
Post a Comment
Legal knowledge is largely expressed in written language, and legal professionals read and write to access, process and reason with the knowledge in texts. Although one can use information extraction to process text on a computer, the text remains a meaningless string of characters to the machine, without more –- such as the Semantic Web.
The Semantic Web, an extension of the current World Wide Web, promises to make Web-based documents meaningful to both people and computers by changing how legal knowledge is represented, managed and reasoned with. This article focuses on ontologies, which are one of the means to complete the Semantic Web's design. It introduces some of the broad concepts of ontologies, indicates some of the sources of further information and tools, then provides a brief example of a legal ontology.
ONTOLOGIES
An ontology represents a common vocabulary and organization of information that explicitly, formally and generally specifies a conceptualization of a given domain. Ontologies are related to knowledge management (cf. Rusanow's 'Knowledge Management and the Smarter Lawyer') and taxonomies (cf. Sherwin's article 'Legal Taxonomies'). But an ontology is a more specific, explicit and formal representation of knowledge than provided by KM; and it is richer and more flexible than a taxonomy.
KM is concerned with how legal professionals share documents and use communication tools like blogs, wikis and e-mail, all of which are irrelevant to ontologies. Taxonomies do not appear to require an ontology’s logical facilities. In contrast to KM and taxonomies, legal ontologies have not been widely discussed among legal professionals, albeit they have long been discussed among researchers in artificial intelligence and law (cf. papers by Professor Trevor Bench-Capon).
In making an ontology, one turns tacit expert knowledge into explicit representations that can be shared, tested and modified by people as well as processed by a computer. Ontologies are delimited, representing only some aspects of some domain, though one ontology may relate to another one (e.g., ontologies for intellectual property cases and human rights cases are related to one another and to a global ontology for legal cases).
To specify a conceptualization of a domain, we define classes of individuals (e.g., lawyers and law firms), subclasses of the classes (e.g., partners and associates are subclasses of lawyers), the properties which hold of individuals of a class (e.g., partners have seniority) and relationships among the individuals (e.g., every associate works for a partner, every lawyer works for a firm, every partner of a law firm works for that law firm). We can infer properties by inheritance, for example, given lawyers who work for a particular firm, then the partners of that firm work for the firm.
Rules for reasoning about cases can be expressed in the terminology of the ontology. With instances of an ontology along with rules, we have a knowledge base from which we can make inferences using the rules: for example, Hale, Williams and Partners is a law firm; Joan Williams is a partner at Hale, Williams, and Partners. Given what we know about law firms, we infer that Joan Williams works at Hale, Williams and Partners and that Joan Williams is a lawyer. To us, these inferences are obvious, but for a computer to make them, the knowledge must use explicit, formal inference rules. Finally, contemporary ontologies can be used to mark up documents so that a computer can meaningfully access and process the content.
For small examples and domains, there is little value in making an ontology and inference rules. The information and inferences are readily apparent to us. However, for large corpora and complex domains, the advantages are that knowledge can be systematized, rich patterns of information can be readily extracted, and inferences can be drawn that would not otherwise be apparent.
To develop and use an ontology, there must be some formalized, machine-readable format, tools to create and manage the ontologies and draw inferences, and a means to mark up documents using the ontology. One format which has been designed for the Web is the Web Ontology Language OWL.
OWL can be used to mark up the semantic role of a section of text; for example, we can take [[lawFirmName :: Hale, Williams and Partners]] to mean that the text after the colons is an instance of the name of a law firm. In the ontology, the expression 'lawFirmName' stands for the class of entities which are names of law firms. The purpose of the markup is to provide a standard form that indicates to the machine what part the linguistic information plays in the knowledge representation of the firm.
OWL also supports a range of logical properties such as conjunction, inference and negation. Such a markup of text on a page is a version of the hyperlinks or text styles such as italics that appear in articles; markups ascribe additional properties or functionality beyond the text itself. With tools such as the free, open source ontology editor Protégé, one can develop an ontology using graphic representations rather than elaborate markups; the editor comes with additional tools to visualize the ontology or test it for consistency. Finally, Web-based tools such as Semantic MediaWiki along with the extension Halo enable one to place documents on the Web which have been marked up using a specified ontology.
ONTOLOGY FOR CASE LAW
Consider an example ontology for case law. There are various approaches to find relevant case law –- using text-mining software, search tools, proprietary indices or legal research summaries. These approaches can extract some latent linguistic information from the text but often require researchers to craft the results; indeed, successful information extraction depends on an ontology, and as there is not yet a rich ontology of the case law domain, much information in cases cannot be easily extracted or reasoned with. Moreover, none of these approaches apply inference rules.
Reading a case such as Manhattan Loft v. Mercury Liquors, there are elementary questions that can be answered by any legal professional, but not by a computer:
* Where was the case decided?
* Who were the participants and what roles did they play?
* Was it a case of first instance or on appeal?
* What was the basis of the appeal?
* What were the legal issues at stake?
* What were the facts?
* What factors were relevant in making the decision?
* What was the decision?
* What legislation or case law was cited?
Legal information service providers such as LexisNexis index some of the information and provide it in headnotes, but many of the details, which may be crucial, can only be found by reading the case itself. Current text-mining technologies cannot answer the questions because the information is embedded in the complexities of the language of the case, which computers cannot yet fully parse and understand. Finally, there are relationships among the pieces of information which no current automated system can represent, such as the relationships among case factors or precedential relationships among cases.
To be specific, consider some sample markups. Among the participants, we have those in the role of plaintiff and others in the role of defendant, which we would mark as [[plaintiff:: Manhattan Loft]] and [[defendant:: Mercury Liquors]]. There is a decision indicated with [[decision:: Appeal reversed and remanded with directions]]. We have a legal question [[legalQuestion:: Can a party to a pending arbitration record a notice of pendancy of action without first filling a civil action in Superior Court?]]. In the discussion section, there are references to legislation, which might appear as [[legislationReference:: Section 425.16, subdivision (b)(1) of the Anti-SLAPP Statute]]. Thus, a computer can search for the term 'legalQuestion' within the case to find the content. Further components of a case can similarly be represented, such as the conditions which must be satisfied to meet requirements of a legal rule, or the mitigating or aggravating factors which contribute to the decision. The assumption is that while different cases represent information such as the legal question in different linguistic forms, the markup will remain constant; in this sense, the ontology is an abstract representation of knowledge.
PRACTICAL ISSUES
Several practical issues arise. In any case, there is a range of information that may prove useful to a researcher. Moreover, there are many case decisions handed down every year. Given the volume and variety, the legal researcher must negotiate between a fine-grained and a coarse-grained ontology. This is an ongoing, experimental issue which need not be decided all one way or the other, for there may be a variety of related and integrated ontologies which suit different purposes. Who develops the ontology? Given current available tools, an exciting option is Web-based collaborative ontology development, where legal professionals contribute to a free, open ontology of the law. Who does the markup and how is it checked? At this point, the labor is manual.
As a learning tool for law students or a tool for researchers, the labor can be done by individuals using Semantic MediaWikis. As a large-scale enterprise, legal publishers or government agencies could mark up cases using tool bars integrated with word processing software so that the case is marked up as it is written up over the course of the case. Marke- up cases would add enormous value to the case corpora for legal professionals, so there is adequate incentive.
Legal ontologies are one of the central elements of managing and automating legal knowledge. With ontologies, the means are available to realize significant portions of the Semantic Web for legal professionals, particularly if an open-source, collaborative approach is taken.
Dr. Adam Zachary Wyner is affiliated with the department of computer science at University College London, London, United Kingdom. He has a Ph.D. in linguistics from Cornell University and a Ph.D. in computer science from King's College London. He has published on topics in the syntax and semantics of natural language, as well as artificial intelligence and law concerning legal systems, language, logic and argumentation. For further information, see Dr. Wyner's blog LanguageLogicLawSoftware. He can be contacted via e-mail at adam@wyner.info and telephone at 00-44-(2)-208-809-3960."
By Dr. Adam Z. Wyner
Special to Law.com
June 8, 2009
Post a Comment
Legal knowledge is largely expressed in written language, and legal professionals read and write to access, process and reason with the knowledge in texts. Although one can use information extraction to process text on a computer, the text remains a meaningless string of characters to the machine, without more –- such as the Semantic Web.
The Semantic Web, an extension of the current World Wide Web, promises to make Web-based documents meaningful to both people and computers by changing how legal knowledge is represented, managed and reasoned with. This article focuses on ontologies, which are one of the means to complete the Semantic Web's design. It introduces some of the broad concepts of ontologies, indicates some of the sources of further information and tools, then provides a brief example of a legal ontology.
ONTOLOGIES
An ontology represents a common vocabulary and organization of information that explicitly, formally and generally specifies a conceptualization of a given domain. Ontologies are related to knowledge management (cf. Rusanow's 'Knowledge Management and the Smarter Lawyer') and taxonomies (cf. Sherwin's article 'Legal Taxonomies'). But an ontology is a more specific, explicit and formal representation of knowledge than provided by KM; and it is richer and more flexible than a taxonomy.
KM is concerned with how legal professionals share documents and use communication tools like blogs, wikis and e-mail, all of which are irrelevant to ontologies. Taxonomies do not appear to require an ontology’s logical facilities. In contrast to KM and taxonomies, legal ontologies have not been widely discussed among legal professionals, albeit they have long been discussed among researchers in artificial intelligence and law (cf. papers by Professor Trevor Bench-Capon).
In making an ontology, one turns tacit expert knowledge into explicit representations that can be shared, tested and modified by people as well as processed by a computer. Ontologies are delimited, representing only some aspects of some domain, though one ontology may relate to another one (e.g., ontologies for intellectual property cases and human rights cases are related to one another and to a global ontology for legal cases).
To specify a conceptualization of a domain, we define classes of individuals (e.g., lawyers and law firms), subclasses of the classes (e.g., partners and associates are subclasses of lawyers), the properties which hold of individuals of a class (e.g., partners have seniority) and relationships among the individuals (e.g., every associate works for a partner, every lawyer works for a firm, every partner of a law firm works for that law firm). We can infer properties by inheritance, for example, given lawyers who work for a particular firm, then the partners of that firm work for the firm.
Rules for reasoning about cases can be expressed in the terminology of the ontology. With instances of an ontology along with rules, we have a knowledge base from which we can make inferences using the rules: for example, Hale, Williams and Partners is a law firm; Joan Williams is a partner at Hale, Williams, and Partners. Given what we know about law firms, we infer that Joan Williams works at Hale, Williams and Partners and that Joan Williams is a lawyer. To us, these inferences are obvious, but for a computer to make them, the knowledge must use explicit, formal inference rules. Finally, contemporary ontologies can be used to mark up documents so that a computer can meaningfully access and process the content.
For small examples and domains, there is little value in making an ontology and inference rules. The information and inferences are readily apparent to us. However, for large corpora and complex domains, the advantages are that knowledge can be systematized, rich patterns of information can be readily extracted, and inferences can be drawn that would not otherwise be apparent.
To develop and use an ontology, there must be some formalized, machine-readable format, tools to create and manage the ontologies and draw inferences, and a means to mark up documents using the ontology. One format which has been designed for the Web is the Web Ontology Language OWL.
OWL can be used to mark up the semantic role of a section of text; for example, we can take [[lawFirmName :: Hale, Williams and Partners]] to mean that the text after the colons is an instance of the name of a law firm. In the ontology, the expression 'lawFirmName' stands for the class of entities which are names of law firms. The purpose of the markup is to provide a standard form that indicates to the machine what part the linguistic information plays in the knowledge representation of the firm.
OWL also supports a range of logical properties such as conjunction, inference and negation. Such a markup of text on a page is a version of the hyperlinks or text styles such as italics that appear in articles; markups ascribe additional properties or functionality beyond the text itself. With tools such as the free, open source ontology editor Protégé, one can develop an ontology using graphic representations rather than elaborate markups; the editor comes with additional tools to visualize the ontology or test it for consistency. Finally, Web-based tools such as Semantic MediaWiki along with the extension Halo enable one to place documents on the Web which have been marked up using a specified ontology.
ONTOLOGY FOR CASE LAW
Consider an example ontology for case law. There are various approaches to find relevant case law –- using text-mining software, search tools, proprietary indices or legal research summaries. These approaches can extract some latent linguistic information from the text but often require researchers to craft the results; indeed, successful information extraction depends on an ontology, and as there is not yet a rich ontology of the case law domain, much information in cases cannot be easily extracted or reasoned with. Moreover, none of these approaches apply inference rules.
Reading a case such as Manhattan Loft v. Mercury Liquors, there are elementary questions that can be answered by any legal professional, but not by a computer:
* Where was the case decided?
* Who were the participants and what roles did they play?
* Was it a case of first instance or on appeal?
* What was the basis of the appeal?
* What were the legal issues at stake?
* What were the facts?
* What factors were relevant in making the decision?
* What was the decision?
* What legislation or case law was cited?
Legal information service providers such as LexisNexis index some of the information and provide it in headnotes, but many of the details, which may be crucial, can only be found by reading the case itself. Current text-mining technologies cannot answer the questions because the information is embedded in the complexities of the language of the case, which computers cannot yet fully parse and understand. Finally, there are relationships among the pieces of information which no current automated system can represent, such as the relationships among case factors or precedential relationships among cases.
To be specific, consider some sample markups. Among the participants, we have those in the role of plaintiff and others in the role of defendant, which we would mark as [[plaintiff:: Manhattan Loft]] and [[defendant:: Mercury Liquors]]. There is a decision indicated with [[decision:: Appeal reversed and remanded with directions]]. We have a legal question [[legalQuestion:: Can a party to a pending arbitration record a notice of pendancy of action without first filling a civil action in Superior Court?]]. In the discussion section, there are references to legislation, which might appear as [[legislationReference:: Section 425.16, subdivision (b)(1) of the Anti-SLAPP Statute]]. Thus, a computer can search for the term 'legalQuestion' within the case to find the content. Further components of a case can similarly be represented, such as the conditions which must be satisfied to meet requirements of a legal rule, or the mitigating or aggravating factors which contribute to the decision. The assumption is that while different cases represent information such as the legal question in different linguistic forms, the markup will remain constant; in this sense, the ontology is an abstract representation of knowledge.
PRACTICAL ISSUES
Several practical issues arise. In any case, there is a range of information that may prove useful to a researcher. Moreover, there are many case decisions handed down every year. Given the volume and variety, the legal researcher must negotiate between a fine-grained and a coarse-grained ontology. This is an ongoing, experimental issue which need not be decided all one way or the other, for there may be a variety of related and integrated ontologies which suit different purposes. Who develops the ontology? Given current available tools, an exciting option is Web-based collaborative ontology development, where legal professionals contribute to a free, open ontology of the law. Who does the markup and how is it checked? At this point, the labor is manual.
As a learning tool for law students or a tool for researchers, the labor can be done by individuals using Semantic MediaWikis. As a large-scale enterprise, legal publishers or government agencies could mark up cases using tool bars integrated with word processing software so that the case is marked up as it is written up over the course of the case. Marke- up cases would add enormous value to the case corpora for legal professionals, so there is adequate incentive.
Legal ontologies are one of the central elements of managing and automating legal knowledge. With ontologies, the means are available to realize significant portions of the Semantic Web for legal professionals, particularly if an open-source, collaborative approach is taken.
Dr. Adam Zachary Wyner is affiliated with the department of computer science at University College London, London, United Kingdom. He has a Ph.D. in linguistics from Cornell University and a Ph.D. in computer science from King's College London. He has published on topics in the syntax and semantics of natural language, as well as artificial intelligence and law concerning legal systems, language, logic and argumentation. For further information, see Dr. Wyner's blog LanguageLogicLawSoftware. He can be contacted via e-mail at adam@wyner.info and telephone at 00-44-(2)-208-809-3960."
terça-feira, 2 de junho de 2009
Google Wave: Surfing the future of collaboration | The View from Forrester Research | ZDNet.com
Google Wave: Surfing the future of collaboration | The View from Forrester Research | ZDNet.com: "Google is a remarkable company. Need proof? Just consider how reliant we are on Google Maps to find our way around the world. That didn’t happen by accident. It happened because Google empowered a couple of brothers, Lars and Jens Rasmussen, to open up the developer APIs to the mapping engine.
These same two brothers announced yesterday at Google I/O developer conference a new technology for communication and collaboration. This new collaboration engine unites email, instant messaging, blogs, wikis into a single hosted onversation. Check out the demo here and the announcement here.
These conversations or “Waves” take place inside Safari, Firefox, or Chrome and look like email on steroids. (Lars said that they took the 40-year old model of email and redesigned it for today’s Web-based world.) But it’s way more than that. With Google Wave, Google has:
* Opened a new path to reinvent how we collaborate. You have to see it to understand, but why would you need four products when one Wave will do? It’s a new conversational metaphor that will also easily support document-based collaboration.
* Put the code base into open source to attract investment. Google will attract the best and brightest developers and development with this move.
* Published developer APIs to allow others to embed “conversations” anywhere. In a hope to replicate the success of Google Maps, these APIs will make Google’s hosted conversations a convenient way for anybody to offer these features to customers, members, employees, etc.
* Re-asserted its interest in hosting the world’s conversations. Google will host these conversations. And that means Google will be curator of more and more of the world’s converations. An awesome reponsibility for sure, and one that regulators should pay attention to. Buut someone has to do it. Why not a company with a founding culture of “do no evil?”
Now this will happen only slowly. The product will go into official beta later this year and be evolving for the next 2 or 3 years. But the path is clear, and the implications are coming into focus. For Information & Knowlege Management Professionals and for the industry, this is what it means.
* What it means (WIM) #1: Don’t get too stuck on installed email clients — they can’t evolve fast enough. Notes and Outlook are fabulous tools. But they are installed software sold under a perpetual license model. And that means they can only evolve as fast as you are willing to buy licenses and deal with installation and change management. And that’s too slow to keep up.
* WIM #2: Google Apps Premier Edition is worth keeping a close eye on. It’s a guarantee that Google Wave will appear in the Google Apps sometime soon, so keep an eye on what it might mean if you want to switch providers.
* WIM #3: Microsoft will have yet another innovation hill to climb (and it will). Redmond will have to digest this advance, but it will shortly ramp up its own conversation-oriented online engine. It will have to make this kind of conversational advance part of its BPOS strategy at some point.
* WIM #4: IBM’s approach to collaboration is looking pretty visionary. Lotus has been quietly reinventing itself over the past few years, and if you haven’t looked at Notes or Sametime lately, you need to. And with lead architect Allistair Rennie now at the vision helm, these products with their REST-ful APIs, redesigned interfaces, and Web-centric design metaphors are looking good.
Friend and colleague Jeremiah Owyang brings a nice Web 2.0 angle into this analysis. It’s about combining real-time, social, asynch, and multi-media/multi-device into one place.
Ted Schadler serves Information & Knowledge Management professionals. His primary research objective is to help clients select and implement real-time collaboration tools and understand the impact of emerging technologies on information workers. His work includes research on real-time collaboration tools, the economics of cloud-based collaboration, the effect of mobile devices on enterprise collaboration, and the future of virtual worlds in the enterprise.
Forrester Research, Inc. is an independent research company that provides pragmatic and forward-thinking advice to global leaders in business and technology. Forrester works with professionals in 19 key roles at major companies providing proprietary research, consumer insight, consulting, events, and peer-to-peer executive programs. For more than 25 years, Forrester has been making IT, marketing, and technology industry leaders successful every day. For more information, visit www.forrester.com."
These same two brothers announced yesterday at Google I/O developer conference a new technology for communication and collaboration. This new collaboration engine unites email, instant messaging, blogs, wikis into a single hosted onversation. Check out the demo here and the announcement here.
These conversations or “Waves” take place inside Safari, Firefox, or Chrome and look like email on steroids. (Lars said that they took the 40-year old model of email and redesigned it for today’s Web-based world.) But it’s way more than that. With Google Wave, Google has:
* Opened a new path to reinvent how we collaborate. You have to see it to understand, but why would you need four products when one Wave will do? It’s a new conversational metaphor that will also easily support document-based collaboration.
* Put the code base into open source to attract investment. Google will attract the best and brightest developers and development with this move.
* Published developer APIs to allow others to embed “conversations” anywhere. In a hope to replicate the success of Google Maps, these APIs will make Google’s hosted conversations a convenient way for anybody to offer these features to customers, members, employees, etc.
* Re-asserted its interest in hosting the world’s conversations. Google will host these conversations. And that means Google will be curator of more and more of the world’s converations. An awesome reponsibility for sure, and one that regulators should pay attention to. Buut someone has to do it. Why not a company with a founding culture of “do no evil?”
Now this will happen only slowly. The product will go into official beta later this year and be evolving for the next 2 or 3 years. But the path is clear, and the implications are coming into focus. For Information & Knowlege Management Professionals and for the industry, this is what it means.
* What it means (WIM) #1: Don’t get too stuck on installed email clients — they can’t evolve fast enough. Notes and Outlook are fabulous tools. But they are installed software sold under a perpetual license model. And that means they can only evolve as fast as you are willing to buy licenses and deal with installation and change management. And that’s too slow to keep up.
* WIM #2: Google Apps Premier Edition is worth keeping a close eye on. It’s a guarantee that Google Wave will appear in the Google Apps sometime soon, so keep an eye on what it might mean if you want to switch providers.
* WIM #3: Microsoft will have yet another innovation hill to climb (and it will). Redmond will have to digest this advance, but it will shortly ramp up its own conversation-oriented online engine. It will have to make this kind of conversational advance part of its BPOS strategy at some point.
* WIM #4: IBM’s approach to collaboration is looking pretty visionary. Lotus has been quietly reinventing itself over the past few years, and if you haven’t looked at Notes or Sametime lately, you need to. And with lead architect Allistair Rennie now at the vision helm, these products with their REST-ful APIs, redesigned interfaces, and Web-centric design metaphors are looking good.
Friend and colleague Jeremiah Owyang brings a nice Web 2.0 angle into this analysis. It’s about combining real-time, social, asynch, and multi-media/multi-device into one place.
Ted Schadler serves Information & Knowledge Management professionals. His primary research objective is to help clients select and implement real-time collaboration tools and understand the impact of emerging technologies on information workers. His work includes research on real-time collaboration tools, the economics of cloud-based collaboration, the effect of mobile devices on enterprise collaboration, and the future of virtual worlds in the enterprise.
Forrester Research, Inc. is an independent research company that provides pragmatic and forward-thinking advice to global leaders in business and technology. Forrester works with professionals in 19 key roles at major companies providing proprietary research, consumer insight, consulting, events, and peer-to-peer executive programs. For more than 25 years, Forrester has been making IT, marketing, and technology industry leaders successful every day. For more information, visit www.forrester.com."
Understanding the intersection between Records and Document Management and Electronic HEALTH Records - Information Zen
Understanding the intersection between Records and Document Management and Electronic HEALTH Records - Information Zen: "Here are some of the things that I think I have figured out about document management solutions and electronic health records and the stimulus program. I send them around not so much under the thought that they are 100% correct, but rather to make get some correction and additions from the collected community.
To get involved in the discussion, join our Electronic Health Records Forum.
Here are the steps you'll need to follow once you get there...
Here are the 3 steps to follow:
1. If you are not currently a Zen member you will be asked to create an account.
2. Once you do so, you will see a form to request admission to the Electronic Health Records Forum.
3. You’ll then get a follow-up email activating your request.
So here goes…
There is $23 billion in play in two portions.
One portion ($2 billion) goes immediately to HHS and its sub-agency, the Office of the National Coordinator for Health IT (ONC) and directs creation of standards and policy committees. We (AIIM) are actively working to connect our PDF/H work up with the efforts being directed by the ONC.
The second portion sets aside $21 billion (net) to physicians and hospitals to implement electronic health records. There is a program designed for those who see large volumes of Medicaid patients, and another for those that accept Medicare."
To get involved in the discussion, join our Electronic Health Records Forum.
Here are the steps you'll need to follow once you get there...
Here are the 3 steps to follow:
1. If you are not currently a Zen member you will be asked to create an account.
2. Once you do so, you will see a form to request admission to the Electronic Health Records Forum.
3. You’ll then get a follow-up email activating your request.
So here goes…
There is $23 billion in play in two portions.
One portion ($2 billion) goes immediately to HHS and its sub-agency, the Office of the National Coordinator for Health IT (ONC) and directs creation of standards and policy committees. We (AIIM) are actively working to connect our PDF/H work up with the efforts being directed by the ONC.
The second portion sets aside $21 billion (net) to physicians and hospitals to implement electronic health records. There is a program designed for those who see large volumes of Medicaid patients, and another for those that accept Medicare."
Yakabod Provides Robust Knowledge Management with a Social Side | The AppGap
Yakabod Provides Robust Knowledge Management with a Social Side | The AppGap: "Yakabod has been providing knowledge management solutions since 2003. It was founded in 2001 by CEO Scott Ryser and CTO Scott Williamson and first focused on web applications. I recently spoke with Scott Ryser and Chris Coleman, their EVP for Marketing. The firm is headquartered in the DC area and their main clients have been in the US Federal government’s intelligence community.
Yakabod’s flagship product is the Yakabox™ Knowledge Network. It is built to meet Protection Level 3 standards for software used in the U.S. Intelligence Community. It is a secure knowledge-sharing system that enables organizations more control over their content and allows for secure collaboration, both internally and with their business partners. I feel even more importantly it provides the users with great control and flexibility in how they manage content relevant to their work. It is activity based rather than based on taxonomies and file structures isolated from daily work processes. With the release of Yakabox 3.0 they are plan to engage a broader market outside the intelligence community."
Yakabod’s flagship product is the Yakabox™ Knowledge Network. It is built to meet Protection Level 3 standards for software used in the U.S. Intelligence Community. It is a secure knowledge-sharing system that enables organizations more control over their content and allows for secure collaboration, both internally and with their business partners. I feel even more importantly it provides the users with great control and flexibility in how they manage content relevant to their work. It is activity based rather than based on taxonomies and file structures isolated from daily work processes. With the release of Yakabox 3.0 they are plan to engage a broader market outside the intelligence community."
What Produces Social Media Results?
What Produces Social Media Results?
by Jay Deragon on 06/01/2009 06:39 0 comments , 104 views
Categories: Social Media, Social Computing, Web 2.0, Enterprise 2.0
Tags: social+media, knowledge+management, social+technology
Businesses and individuals are all seeking innovative ways to leverage social media for currency, whether actual revenue or goodwill. They see social media as a robust way to add reach and richness to their marketing efforts; however, what is less obvious is how to do it?
How to effectively apply social technology to reach specific markets of conversations and subsequently create the exchange has become the Holy Grail of social media as a process.
For companies to arrive at a more competitive, more advantageous position the route begins by acquiring essential knowledge leading to innovative practices that result in conversational currency. To arrive at your destination you first need to construct a map: A series of initiatives and actions aimed at accelerating your progress towards achieving our ultimate goal. Essential knowledge must first be acquired and translated into a strategic plan that will allow you to set the proper course for your journey.
Most businesses are more focused on the cost and results than gaining an understanding of the process of social media. But in order to effectively leverage social media to produce the intended results it is vitally important to “learn” the art and science of conversations in order to effectively apply the methods of creating conversational currency. Think of this investment in education as a “knowledge transfer process” that may initially require time and money to accomplish but the value it creates far exceeds the cost. The results of effectively enabling individuals and businesses to gain from this transfer of knowledge ultimately will be reflected in the future through significant savings in time and effort. In other words, an investment in essential knowledge leads directly to an increase in overall productivity. Finding the Holy Grail of conversational currency requires safe passage through the hallowed grounds of essential knowledge. No matter what your learning to leverage and give by actively participating in conversations with your markets, it is knowledge that fuels the result.
Each day more businesses are awakening to the power of the social web and there is one thing separating the successful from the unsuccessful: Knowledge. Without knowledge a business can quickly get lost in the “sea of social technology” losing precious time doing unproductive activities without achieving a desired result. Money is a derivative of knowledge. The lack of knowledge is a derivative of cost.
Social Media Funnels Knowledge
The landscape of social media is changing daily. The speed of change creates new dynamics, new knowledge and disruption for those that don’t “know”. Keeping track of these changes requires an investment in time and the application of knowledge. Time cost money. Without gaining the required knowledge you lose time and productivity. Gaining and internalizing “knowledge” is the means for gaining time and improving productivity. Leveraging time and productivity is what produces results.
An individual or business can save time and money by using “knowledge resources”. A knowledge resource is someone or an organization that can either transfer the knowledge of “how to create results from social media” to your internal resources. The other alternative is to partner with knowledge resources that can facilitate your pursuit of social currency on your behalf. However allowing an outside knowledge resource to facilitate the required actions for you means you never internalize or own the knowledge. So, which is better then to outsource or to own?
Ownership enables you to become self sufficient. Outsourcing makes you co-dependent. Co-dependency is problematic: It results in a dysfunctional enterprise. In order to run a functional business you must learn to gain, leverage, create and own knowledge. Using social media to achieve results is a paradigm shift for all of us.Understanding that social media functions as a vast reservoir of information about your markets, your customers, your employees is important.Understanding how to funnel and filter that information into essential knowledge is the key to your success. Monitoring the transitions yet to come is crucial to meeting the challenges these sea changes threaten to have upon your business.
The good news is that you can learn how to ride the wave rather than drown. By valuing the process of learning you can discover innovative ways to generate conversational currency. An old proverb teaches that people perish due to a lack of knowledge. Your company can either prosper or perish. What produces results? Knowledge. Got it? No, then get it! The choice is yours.
What say you?
The Art Of Repurposing Technology To Satisfy Risk Managemen
The Art Of Repurposing Technology To Satisfy Risk Managemen: "Under the theme of doing more with less, we are hearing stories about how in today's economic climate consumers are looking to save money and time by repurposing household items. Consumers are finding new and alternative lives for products that seemingly no longer have any utility rather than discarding them. For example, we hear stories of converting old blue jeans into purses and children's clothes. Even First Lady Michelle Obama has gotten into the act by starting a garden in the White House lawn and using food scraps as compost.
All of this repurposing and reutilization news got me thinking about how corporate risk and compliance departments could leverage impending or ongoing enterprise technology initiatives for their own department's objectives. As such, this article will explore how certain enterprise technology projects can be expanded and given alternative lives beyond the initially intended purpose. In particular, two general areas of technology that were once thought of as 'nice-to-haves' can now be thought of as 'must-haves' when using them for risk, compliance and legal purposes."
All of this repurposing and reutilization news got me thinking about how corporate risk and compliance departments could leverage impending or ongoing enterprise technology initiatives for their own department's objectives. As such, this article will explore how certain enterprise technology projects can be expanded and given alternative lives beyond the initially intended purpose. In particular, two general areas of technology that were once thought of as 'nice-to-haves' can now be thought of as 'must-haves' when using them for risk, compliance and legal purposes."
Fernando Quadro - Melhorando a performance do GeoServer: Dados
Fernando Quadro - Melhorando a performance do GeoServer: Dados: "Hoje iremos falar um pouco sobre os dados do GeoServer, e algumas dicas para aumentar a performance e também facilitar as atualizações de versãoes do GeoServer.
1. Usando um diretório de dados externo
Geoserver vem com seu diretório de dados dentro da sua própria estrutura. No entanto, é uma boa idéia de separá-lo. Usando um diretório dados externos é possível realizar upgrades de uma forma muito mais fácil, e não há risco das informações serem sobrescritas e perdidas, o que poderia trazer uma grande dor de cabeça. Um diretório de dados externa também torna fácil a transferência de sua configuração outro lugar, se assim for necessário. Para apontar para um diretório de dados externos, você só precisa de editar o arquivo web.xml, na propriedade GEOSERVER_DATA_DIR.
2. Usando banco de dados espacial
Shapefiles é o formato mais popular quando se fala em dados geoespaciais. Mas se você estiver utilizando o Geoserver em um ambiente de produção, é recomendável converter seus shapefiles para uma base de dados espaciais, tais como PostGIS. A maior parte das bases de dados espaciais fornecer ferramentas da conversão de shapefile. Embora existam muitas opções, PostGIS é o mais recomendado, porém Oracle, DB2 e ArcSDE também possuem esse suporte."
1. Usando um diretório de dados externo
Geoserver vem com seu diretório de dados dentro da sua própria estrutura. No entanto, é uma boa idéia de separá-lo. Usando um diretório dados externos é possível realizar upgrades de uma forma muito mais fácil, e não há risco das informações serem sobrescritas e perdidas, o que poderia trazer uma grande dor de cabeça. Um diretório de dados externa também torna fácil a transferência de sua configuração outro lugar, se assim for necessário. Para apontar para um diretório de dados externos, você só precisa de editar o arquivo web.xml, na propriedade GEOSERVER_DATA_DIR.
2. Usando banco de dados espacial
Shapefiles é o formato mais popular quando se fala em dados geoespaciais. Mas se você estiver utilizando o Geoserver em um ambiente de produção, é recomendável converter seus shapefiles para uma base de dados espaciais, tais como PostGIS. A maior parte das bases de dados espaciais fornecer ferramentas da conversão de shapefile. Embora existam muitas opções, PostGIS é o mais recomendado, porém Oracle, DB2 e ArcSDE também possuem esse suporte."
How to Build a 100-Million-Image Database
How to Build a 100-Million-Image Database
The next generation of image-search algorithms must be evaluated using a database big enough to test their mettle.
Monday, June 01, 2009
We take some 80 billion photographs each year which would require around 400 petabytes to store if they were all saved. Finding your cherished shot of Aunt Marjory's 80th birthday party among that lot is going to take some special kind of search algorithm. And of course, various groups are working on just how to solve this problem.
But if you want to build the next generation of image search algorithms, you need a database on which to test it, say Andrea Esuli and pals at the Institute of Information Science and Technologies in Pisa, Italy. And they have one: a database of 100 million high quality digital images taken from Flickr. For each image they have extracted five descriptive features such as colours, shape, and texture, as defined by the MPEG-7 image standard.
That's no mean feat. Esuli and co point out that such an image database would normally require the download and processing of up to 50 TB of data, something that would take take about 12 years on a standard PC and about 2 years using a high-end multi-core PC. Instead, they simply decided to crawl the Flickr site, where the pictures are already stories, taking what data they need as descripitors. This paper describes the trials and tribulations of building such a database.
Elusi and co also announce that the resulting collection is now open to the research community for experiments and comparisons. So if you're testing the next generation of image search algorithm, this is the database you need to set it loose on.
Finding Aunt Marjory may not be the lost cause we had thought.
Ref: http://arxiv.org/abs/0905.4627 :CoPhIR: a Test Collection for Content-Based Image Retrieval
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