As enterprise supply chains and consumer demand chains have beome globalized, they continue to inefficiently share information “one-up/one-down”. Profound "bullwhip effects" in the chains cause managers to scramble with inventory shortages and consumers attempting to understand product recalls, especially food safety recalls. Add to this the increasing usage of personal mobile devices by managers and consumers seeking real-time information about products, materials and ingredient sources. The popularity of mobile devices with consumers is inexorably tugging at enterprise IT departments to shifting to apps and services. But both consumer and enterprise data is a proprietary asset that must be selectively shared to be efficiently shared.
About Steve Holcombe
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"Collaborative computing is a shared computing environment or application that facilitates communication and teamwork among groups of people.
Displaying common data on multiple computers is one type of collaborative work support system. U.S. Pat. No. 5,996,002 to Katsurabayashi et al. for "Collaborative Work Support System and Method to Facilitate the Process of Discussion in a Meeting Using a Shared Window" discloses a system that includes shared data that is displayed on each computer, individual data that is individually displayed on any of the computer selected by the data owner, and a manager for managing data storage. A similar system that displays shared data on multiple computers is disclosed in U.S. Pat. No. 5,872,924 to Nakayama et al. for "Collaborative Work Support System" in which a limited amount of data is displayed according to multiple attributes setting in the shared windows.
A detail log is a simple way to provide user activity information. U.S. Pat. No. 5,008,853 to Bly et al. for "Representation of Collaborative Multi-user Activities Relative to Shared Structured Data Objects in a Networked Workstation. Environment" proposed a multi-user collaborative system in which the contents as well as the current status of other user activity can be concurrently accessed by different users. The WYSIWIS (What You See Is What I See) user interface representation includes an ordered listing of such entries that are maintained by the structured data object and various attributes of each listed entry; inter alia, the type and class of entry, the revision number of the shared structured data object, the number of pages and revision number of each structured data object entry, the date of creation and last revision of each such entry, whether an entry can be accessed by a user and, if not, who has prevented such access to prevent concurrent editing, whether a local instance of an entry is present on a user's system, and a provision for miscellaneous notes or comments relative to each entry for view by other users.
Visual representation of other users is a standard way of monitoring other's activity. U.S. Pat. No. 5,793,365 to Tang et al. for "System and Method Providing a Computer User Interface Enabling Access to Distributed Workgroup Members" discloses a system that uses a user interface to display visual representations of selected other users in the workgroup. The visual representations are frequently updated to indicate the activity level of these users. An encounter awareness system detects the presence of other users who are doing similar tasks. U.S. Pat. No. 4,974,173 to Stefik et al. for "Small-scale Workspace Representations Indicating Activities by Other Users" proposes a computer system and method that provide networked computer users with information about which other users are task proximate to the user, thereby facilitating spontaneous communications regarding task-related, or other issues. Task proximity to other users may change as the user context switches between applications, and the user interface window is updated accordingly. Task proximity is determined individually by different applications.
Discourse manager has been used to promote effective collaboration between a user and a collaborative computer agent. U.S. Pat. No. 5,819,243 to Rich et al. for "System with Collaborative Interface Agent" suggested a system which operates according to a theory of collaborative discourse between humans, with the computer agent playing the same role as a human collaborator. The discourse manager includes a memory in which application-specific recipes are stored and a memory in which the discourse state is stored. Each recipe specifies a set of actions or sub-tasks, which are performed to achieve an objective. The discourse state includes structures to track the agent's and user's current objectives, a selected recipe for each objective, and completed steps in each recipe. During operation of the discourse manager, user actions and communications are interpreted according to how they relate to the current discourse state. The manager also generates an agenda of expected communications, which is presented to the user as a menu, obviating the need for the natural language understanding by the agent.
Prior work in collaborative computing can provide awareness among group members through various communication channels, such as video, audio, graphical user interface (GUI), etc. However, the prior work requires users to adjust the communication channel and the degree of communications. For example, when a person leaves his or her office, he or she has to manually turn the audio and video off if it is not needed. This is not convenient ....
It is therefore an object of the present invention to provide a way to automatically adjust communication channels among users based on users' current states detected by various sensing devices.
According to the invention, there is provided a collaboration system that includes an awareness system for evaluating, monitoring, and controlling, in real-time, the collaboration environment by having events and occurrences with properties. The awareness monitoring system includes (1) input sensors for receiving real-time data produced by the event and (2) an elastic spring energy model for automatically adjusting a distance that is according to a level of privacy desired by individual users and a need of the collaborative project to have some shared information about individual user activities."
"Since April, 552 persons infected with Salmonella Saintpaul with the same genetic fingerprint have been identified in 32 states and the District of Columbia. These were identified because clinical laboratories in all states send Salmonella strains from ill persons to their State public health laboratory for characterization. The marked increase in reported ill persons since the last update is not thought to be due to a large number of new infections. The number of reported ill persons increased mainly because some states improved surveillance for Salmonella in response to this outbreak and because laboratory identification of many previously submitted strains was completed." Data source:U.S. Centers for Disease Control and Prevention (CDC).
And, by way of comparison, here's an MSNBC.COM video podcasts from June 18, 2008, the day before.
"The Food, Drug, and Cosmetic Act (the Act) authorizes FDA to detain a regulated product that appears to be out of compliance with the Act. The FDA district office will then issue a "Notice of FDA Action" specifying the nature of the violation to the owner or consignee. The owner or consignee is entitled to an informal hearing in order to provide testimony regarding the admissibility of the product. If the owner fails to submit evidence that the product is in compliance or fails to submit a plan to bring the product into compliance, FDA will issue another "Notice of FDA Action" Refusing admission to the product. The product then has to be exported or destroyed within 90 days. The [obsolete] IDR [Import Detention Reports] gave an incomplete picture in that it only reflected the initial action by the Agency and not the ultimate determination of the compliance status of the product. The IRR reports on those products for which the that determination was to refuse admission to the product. The IRR [Import Refusal Reports] is generated from data collected by FDA's Operational and Administrative System for Import Support (OASIS) and is updated monthly. Each month, the IRR is available sorted by country and by product based on the industry code which is the first two characters of FDA's product code (e.g., all fishery/seafood products will be coded 16...). FDA has prepared this information in an effort to provide the importing community with information on products that have been found to appear in violation of the Act." Data Source: Import Refusal Reports for OASIS - US Food and Drug Administration (FDA). [Bracketed language added for clarity]
The following video was taken of Nova Spivack at The Next Web 2008 Conference held April 3 and 4 in Amsterdam, The Netherlands. Other commentators have blogged on Nova's slideshow presentation given at the conference. See, e.g., Is Keyword Search About To Hit Its Breaking Point? (25 April 2008) by Erick Schonfeld, and Nova Spivack: "The Semantic Web as an open and less evil web" (3 April 2008) by Anne Helmond. I, myself, recently blogged on a statement of prior art filed by Nova Spivack and Kristinn Thorisson in a US patent application that I called Categorizing the Internet's Serious Problems (11 June 2008). For many of you, this latter blog may be a good starting point for what is to follow.
Because the video only became available in early June, I have been provided with an opportunity to blog on both the slideshow and the video at the same time. I have also included reference to substantial quoted text and, furthermore, you will find here the inclusion of two 'missing' slides. However,I would like to add that I am not blogging this as an opinion piece but as an addition to this blog's Reference Library. I'll blog my opinions in later entries.
I have stacked the video (42m07s) and accompanying slideshow (44 slides) one on top of the other, below, for those of you who have the time to listen and watch the video, and may want to jump back and forth between the two. The video irregularly shows where Nova is in his slide presentation, and often a slide to which he is referring is difficult to read or only partially shown. So having the slideshow available for quick reference should be useful.
I have included a substantial transcription of Nova's presentation beginning at Slide 17 (the 15m mark) through to the end of the 42m video and finishing with Slide 31. I have paid special attention to the time frame covering Slides 17 - 31 because Nova is then particularly focused on explaining the emerging mechanics and standards of the Semantic Web. Nova is a fast, fast talker (literally). I have taken care to accurately qoute Nova but if you find that I have mis-quoted him, please let me know.
Be aware that Nova does not orally present slides 32 through 44 which cover his product, Twine.
In addition to the embedded slideshow, below, I have obtained an updatedversion (dated 19 May 2008) of the same slideshow from Nova himself (Thanks, Nova!). I'm glad he shared it with me because there are two slides presented in the video that do not appear in the embedded slide showbut do appear in the updated slideshow version. These 'missing' slides are presented by Nova after the 15m mark and so I have separately embedded them further down the page of this blog with accompanying quoted text. See the slides below entitled The Growing Linked Data Universe and The Growing Semantic Web, respectively. The updated version of the slideshow is in PPT format. You can download it from among the source references to this blog.
So, again, I have stacked the embedded video and slideshow below.
Below you will find my transcription (i.e., numerous quotes from Nova's oral presentation) beginning at 14m56s, the missing two slides, and a few of my bracketed editorial comments. All quoted language is that of Nova Spivack's unless otherwise noted. All slide titles are emboldened and italicized.
14:56. Slide 17: Two Paths to Adding Semantics.
14:56. "There are two paths [to the Semantic Web]. One is called the bottom up approach, or classic approach, and that is where everybody is going to go learn RDF and OWL and manually create all of this semantic web metadata. It's not going to happen. It's really, really too hard ... The top down approach ... is to do this automatically [so that] the RDF and OWL code [is] embedded into the data - into the content - automatically [which] turns out to be more practical."
15:35. Slide 18: In Practice: Hybrid Approach Works Best.
15:55. Slide 5: The Higher Resolution Web.
[Nova jumps back to this slide in the embedded presentation. This slide was orally skipped over in the initial part of his presentation. In the updated version of the presentation (see above) this slide is numbered 16.]
15:55. "What we are doing is creating a higher resolution web. So it's like digital photography ... The semantic web is like saying we are going to give you ten times the number of megapixels in your data. So we are going to make a higher resolution web because each piece of data is actually going to carry more meaning ...."
16:47. "The way that Google sees the world is very flat. There are just basically pages and links. And that's all it really knows. In a semantic graph you know what types of pages or what types of things these data records are, and what types of links the connections really mean. Its a richer web meaning and we call this a semantic graph.
17:17. Slide 6: The Web Is The Database.
[Nova jumps back to this slide in the embedded presentation. This slide was orally skipped over in the initial part of his presentation. In the updated version of the presentation (see above) this slide is numbered 17.]
17:17. "Using the open standards the web becomes a database ... The [Semantic] Web is the database."
18:08. Slide 19: Smart Data.
18:12. "So we call [the data within the semantic web] 'smart data'. So smart data is data that carries what is needed to make sense of it .... So that you don't have to refer to some other application .... All that you need to understand to use that data is carried by the data itself ... You can make really, really dumb software but yet it can do really smart things ... because the intelligence is in the data."
18:53. "Ultimately we might have this piece of general use software that when you point it at semantic web data about health, suddenly it can give you medical advice. But then if you point it at semantic web data about data about the stock market, then it can give you investment advice .... That is the dream of the semantic web. All human knowledge will be on the web in a machine understandable fashion and then all software will be able to use all of this knowledge."
20:26. Slide 20: The Semantic Web Is A Key Enabler.
20:30. "Another concept is ... just in time data. The semantic web, because the data is self-describing enables and application to pull in data it's never seen before and use it right away. The challenge in the old-fashioned way of doing this is "what's the schema?" ... With the semantic web we do away with all of that annoying communication and simply the [smart] data tells you all of that. So it does that using an ontology. An ontology is like a schema. Basically, the data points to a document that describes its structure and the rules for using the data. And so when you see the data you go to the ontology [that is] written in the same language as the data and you can actually use that to make sense of the data without having ... to go read a schema document and then type that into the program.
21:45. Slide 21: The Semantic Web = Open Database Layer For The Web.
21:45. "[Another] way of thinking about the semantic web is that it is an open database layer for the web. So if we are making the web ultimately into an operating system ... [we can call the] Web OS ... it's going to have a file system ... and I think the semantic web is a candidate for that file system.... You should be able to write an application, point it at the web, get data, publish data and not care where it is ... You should be able to do this the way you do [this] on a desktop computer when you write an application ....
22:26. The semantic web standards provide a way of representing the schemas with ontologies, ways of representing rules, the data itself can be represented, mappings that say that this is the same as that can be represented, and there is also a query interface for doing searches in an open way across this data. So this is really a stack that creates a database layer for the web at large.
22:44. "So, this is really a stack that creates a database layer for the web at large. [Nova reveals Slide 22: Semantic Web Open Standards]. And there are several [semantic web] standards that are important. RDF is the main standard and that is really the way the data is represented with things called that are called 'triples' .... OWL is built on RDF ... [and] is just RDF with some more statements in it ... some more expressive power for defining schemas. SPARKL is a query language ... like SQL [but] for RDF. There's a rules language called SWRL ... that hasn't been standardized yet but there is a lot of talk now around the rules. And GRDDL which is for transforming data so you can say here is how to take this XML data and turn it into RDF on the fly. And you can make these GRDDL profiles for websites that enable anybody who wants to see [a] website in RDF to get the RDF [enabled version of the website] immediately.
23:56. Slide 23: RDF "Triples".
23:41. "Let's talk about how the data is represented .... The basic unit of data in the semantic web is called a 'triple' and that's because it has three parts. It has a subject, a predicate and an object. So, for example, "Susan works for IBM" ... [where] Susan - who is actually a URI that represents a data record that describes Susan - works for - which has a URI that defines what you mean by 'works for' somewhere in an ontology - IBM - which has a URI that points to a representation of IBM. Now these three things could be in different [databases] ... So, it's [like] a giant mashup on a very, very atomic level of data.”
25:10. Slide 24: Semantic Web Is Self-Describing Linked Data.
[Comment: Slide 24 represents a picture of a Data Record with an ID and fields connected in one direction to ontological definitions in another direction to other similarly constructed data records with there own fields connected in one direction to ontological definitions, etc. These data records - or semantic web data - are nothing less than self-describing, structured data objects that are atomicly (i.e., granularly) connected by URIs.]
25:14. "This [Slide 24] is illustrating how these data records are all connected together whether it is within one application or across applications. It becomes an open database just like the web but for data .... Is there a better term than semantic web? Yeah, it's data web. That's a better term."
25:35. Slide 25: RDBMS vs. Triplestores.
25:35. "The traditional way of storing data in a relational database would be [by] using [tables] and tables are annoying because they are not really the way we think .... You have to do all of these little tricks to make [data] point to other [data]. Now in the semantic version ... you just make a big list. [You create a] list of triples, each triple is a statement and has URI's in it. And so there's a challenge here that these lists of triples get really, really long. You could easily have a billion, or ten billion, or a hundred billion rows in one of these lists. And if you ... stick [such a] list into a relational database ... you get really bad performance because relational databases were not designed for data that has [billions] of rows and not many columns. Relational databases were [designed] for lots of columns and not as many rows. The optimization in the relational world was for a different shape of database."
26:28. "So to solve this we've created things called triple stores. These are new kinds of databases that are designed for these lists. These lists have a lot of benefits over the relational model. They're much easier to maintain, and they can actually live on top of relational database ...."
26:44. Slide 26: Merging Databases In RDF Is Easy.
26:44. "So one of the nice things about [the lists in the triple stores] is that merging data is extremely easy .... You don't have to do any fancy [relational] database refactoring .... [It is easier to use triple stores because] the way the data is integrated is through URIs.... If you have a URI for IBM [in one data record] and you have [the same] URI for IBM [in another data record], now we know they are referring to the same [data record for IBM]. And so the matching is done at the URI level rather than having [a] human sitting there and having to refactor the database. [Using URIs rather than humans] scales a lot better to the web ...."
27:41. “Now there is this universe of linked data that is emerging .... and there are a number of different ontologies that cover different domains ... [see The Growing Linked Data Universe slide at 27:53, below] .... So there are a bunch of different ontologies and applications that all connect to each other and are sharing data in this growing web of connected data.”
27:53. Missing Slide: The Growing Linked Data Universe.
28:34. Missing Slide: The Growing Semantic Web.
28:54. "You can see [referring to The Growing Semantic Web slide] a lot of activity around consumers right now and online services for developers and consumers. The applications side is starting to emerge in the enterprise space and that's kind of how it looks today."
29:04. “So where are we and where [is the Semantic Web] going .... [Nova reveals Slide 31: Future Outlook] Right now we are still in the early adoption period of this technology but there's a tremendous amount of momentum and a lot of adoption taking place among developers and also some early applications .... So I believe that this period of 2007 to 2009 is really the first wave .... [and during this period there will come to be a couple of million users or more of the semantic web] and then it will [be considered as] mainstream. So when we get into Web 3.0 [in] 2010 that's when real mainstream adoption happens. That's why I believe that semantics will be baked into a lot of mainstream applications from companies whether it is Google or Microsoft. Adobe already does it. Yahoo! already does it."
30:28. "Where the semantic web and data portability [project] meet is that the semantic web provides some open standards for making your data even more portable."
30:40. "[In conclusion the semantic web will] do for data what the web did for documents .... It's very hard to do this today because [while the standards are there in many respects] the tools are not [yet developed] and so if you have a company and you are thinking [that you want] to use the semantic web, it isn't easy. The place to start is [with] a few simple standards. One is called FOAF, it's friend of a friend, and that is for describing user profiles. Another one is SIOC, that is for sharing data about forums, discussions, [and] user accounts .... As you get deeper into the technology, and as more API's come out, and more services become open ... it will get much easier."
[Q & A]
32:48. "My personal opinion is that the semantic web does not introduce any new business models. I think that it just makes the existing business models better...."
38:06. Attendee's Question: "As far as I understood the semantic web it depends on definitions of different [things] .... If I understand correctly these definitions are done by people .... but before you also mentioned that people are really inconsistent so how do you [reconcile this]? Also how additionally do you handle the [definitions] of fast changing things?"
Nova: "Good questions. So there's a big misconception which is that the Semantic Web demands some kind of agreement. And you sometimes see that when people criticize the semantic web they say, "Well nobody's ever going to agree on a definition of ... all these things". In fact the Semantic Web was designed for disagreement. So, anyone can make their own ontology [and therefore] describe the world however they want. So that's a good thing but then it creates this problem that there might be many definitions. You know, here's my way of describing a car. Now over here this is how Toyota, Mercedes Benz, BMW describes a car. And it's different. So one of the things that they built into the standards was a way to map definitions to each other. So you can say [that] this definition is equivalent to that definition. You can also say [that] this piece of data is the same as [that] piece of data. So, anybody can make those mappings. Not just the people who made the data but anybody can make those mappings. And so in a community-driven, bottom up process, when mappings are created you can then begin to infer the equivalence or connections and in fact there's a lot of research going on that just from a few mappings you can make inferences that connect a lot of things together. So I think we will see something like the Wikipedia where lots of different definitions are composed and the winners will be the ones who have the services, who have the content, that uses that definition ... that get the most users ...."
"Notwithstanding this intense focus, there is a continuing need for a method and system for controlling security of data elements which represent an industrial process and which are manipulated by a plurality of users on a data processing system. There is a particular need for controlling security when the industrial process includes many industrial process steps which are practiced over an extended time period."
"Data processing systems are widely used to control industrial processes which move through a series of industrial process steps. An example of an industrial process which is controlled by a data processing system is a computer controlled design and manufacturing system. In a computer controlled design and manufacturing system, often referred to as a computer automated design/computer aided manufacturing (CAD/CAM) system, items are designed on a computer, and the system for manufacturing the designed item is controlled by computers.
In a CAD/CAM system the design of an item progresses through a series of steps, with the design of the item being represented on a computer database during all steps. Thus, for example, the design progresses from a development phase, to a pre-release phase, a release phase, and an accept phase. During the development phase, the design is typically developed and tested for implementation worthiness by test and development engineers. During the pre-release phase, the developed design is approved by various organizations until authority has been given to finalize the design. During the release phase, the design has been reviewed by appropriate authorities and is released for manufacturing.
An item is typically designed at one development location and is manufactured at multiple locations. Accordingly, during the accept phase, the design is accepted into a manufacturing location and manufacturing planners and engineers prepare the design so that it can be implemented in the shop floor at that location. The design then moves to an effective phase where the design has been approved by the manufacturing engineers and is ready to be implemented in the shop floor at a prescribed implementation date. Finally, the design is eventually phased out by placing it in a closed status. In a CAD/CAM system, the design of the item is developed, modified and then implemented for manufacturing on a computer platform rather than using traditional printed engineering blueprints and printed specifications.
After an item is designed and manufactured, an engineering change control process is also typically controlled by a data processing system. As is well known to those having skill in the art, an engineering change represents a change to the design of an item in a manufacturing environment. Similar to the design and manufacturing process for the item itself, an engineering change typically progresses through a series of industrial process steps including development, pre-release, release, accept, effective and closed. CAD/CAM systems typically represent engineering change control data and are used to control the engineering change control process. See for example U.S. Pat. No. 5,191,534 to Orr et al. entitled Engineering and Manufacturing Change Control Mechanism, which is assigned to the assignee of the present invention, the disclosure of which is incorporated herein by reference.
Industrial processes which are controlled by data processing systems are not limited to manufacturing processes. For example, in preparing a document for publication, the document itself typically undergoes phases of development, pre-release, release, accept, effective and closed along the lines described above. Similar steps are also involved in most business processes such as a business proposal or bid process or a budgeting process.
When using a data processing system to control industrial processes of the types described above, it is particularly important to protect the data security of the data processing system. The industrial process is typically represented by a large number of data elements in a database on the data processing system, and multiple users from multiple groups have access to the data. Since the data is ultimately used to design an item, such as a product, a document, or a budget, it is important that the data is not corrupted by the large numbers of people who have access to the data.
Many security control systems for data processing systems which share data have been proposed. See, for example, U.S. Pat. Nos. 4,525,780 to Bratt et al. entitled Data Processing Systems Having a Memory Using Object-Based Information and a Protection Scheme for Determining Access Rights to Such Information; 4,698,752 to Goldstein et al. entitled Data Base Locking; 4,713,753 to Boebert et al. entitled Secure Data Processing System Architecture with Format Control; 5,008,853 to Bly et al. entitled Representation of Collaborative Multi-User Activities Relative to Shared Structure Data Objects in a Networked Workstate Environment; and 5,133,075 to Risch entitled Method of Monitoring Changes in Attribute Values of Object in an Object-Oriented Database.
Notwithstanding this intense focus, there is a continuing need for a method and system for controlling security of data elements which represent an industrial process and which are manipulated by a plurality of users on a data processing system. There is a particular need for controlling security when the industrial process includes many industrial process steps which are practiced over an extended time period."
Tuesday, June 24, 2008 at 2:46AM 
