Due to Fred Giasson‘s great work and the support of the BKN (Bibliographic Knowledge Network) project, five new Web services and associated documentation have been added to structWSF. The five Web services are:
Thanks, Fred, for more amazing, clean work!
The code for these will be posted soon to the structWSF SVN on Google code. UPDATE: The code has now been posted.
An Innovative, Distributed, Scalable Design with Dataset Access RightsstructWSF is a platform-independent Web services framework for accessing and exposing structured RDF data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via separate ontologies (schema with accompanying vocabularies).
The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards, conforming to what is known as a Web-oriented architecture. The initial structWSF framework comes packaged with a baseline set of about a dozen Web services in CRUD, browse, search and export and import. All Web services are exposed via APIs and SPARQL endpoints. It also has direct interfaces to the Virtuoso RDF triple store and the Solr faceted, full-text search engine.
These baseline capabilities are useful enough. But there is another foundation to structWSF that is quite innovative and exciting: Its explicit design to support collaboration networks. It is this aspect that is the focus of this current article.
The collaboration design is a result of the needs of the Bibliographic Knowledge Network (BibKN or BKN) [1]. BibKN has as one of its express purposes creating a network of collaborators in math and statistics, ranging from the individual researcher to departments and universities and various virtual organizations (VOs) representing different communities of interest. Moreover, this nucleus of researchers also has external collaborators ranging from major publishers to software and service providers of various sizes from around the globe.
Thus, one key requirement of the BKN project was to design an infrastructure responsive to this broad spectrum of interests, locations and organizations. And, besides questions of varying scale, locale and distribution, there was also the need to combine public and private data. In some cases, initial work products need to be kept within its sponsoring groups before being made public. Sometimes external publishers want to segregate network members by whether they are already paid subscribers or not. And, most importantly, the project had a mandate to create an easy and open framework for encouraging incipient collaborators and curators to add and take ownership of new datasets.
Boiled down, these requirements represent a completely fluid spectrum of scales, access rights, virtual groups and distributed locations. These requirements were daunting indeed to establish a workable and responsive framework. But, what has resulted from this mandate — structWSF — is a generalized solution that has applicability to collaboration within any knowledge network.
BibKN anticipates and is to include four exemplar types of participants on the network (or “nodes’, which are not to be confused with the different meaning of node in Drupal):
Each of these nodes exposes its data to the rest of the network via a structWSF Web services framework. Each structWSF installation provides an access point and endpoint to the network. Through these installations, data is converted to “canonical” form for use by other nodes on the network with common tools and services provided.
In conceptual, form, then, the network can be represented as follows:
Each node has a structWSF instance, the common network denominator, shown in blue.
A key aspect of each structWSF installation is dataset registration and access authorization. Only users with proper authorization may access or exercise certain privileges such as write or updates for a given dataset.
The other core Web services provided with structWSF are the CRUD functional services (create – read – update – delete), import and export, browse and search, and a basic templating system [see (3) in the next figure]. These are viewed as core services for any structured dataset. The current alpha release supports CSV, TSV, RDF/XML, RDF/N3 and XML, with JSON forthcoming shortly. (UPDATE: Now provided.)
The controlling Web service in structWSF is the Authentication/Registration WS [see (2) in the figure below]. The current alpha version of structWSF uses registered IP addresses as the basis to grant access and privileges to datasets and functional Web services. Later versions will be expanded to include other authentication methods such as OpenID, keys (a la Amazon EC2), foaf+ssl or oauth. A secure channel (HTTPS, SSH) could also be included.
A simple but elegant system guides access and use rights. First, every Web service is characterized as to whether it supports one or more of the CRUD actions. Second, each user is characterized as to whether they first have access rights to a dataset and, if they do, which of the CRUD permissions they have [see (4, 5)]. We can thus characterize the access and use protocol simply as A + CRUD.
Thereafter, a mapping of dataset access and CRUD rights (see below) determines whether users see a given dataset and what Web services (“tools”) are presented to them and how they might manipulate that data. When expressed in standard user interfaces this leads to a simple contextual display of datasets and tools. For example, under standard search or browse activities the user would only see results sets drawn from the datasets for which they have access. Similarly, users only see the tools that their CRUD rights allow.
At the Web service layer, these access values are part of the GET request. The system, however, is designed to more often be driven by user and group management at the CMS level via a lightweight plug-in or module layer.
Because a CMS may employ its own access system and protocols, the potential combinations can become quite large. Let’s take for an example a VO node in the BibKN scenario which layers Drupal (via the conStruct modules) over the structWSF framework. By including the additional third-party contributed Drupal module of Organic Groups, we also now add an entire dimension of group access to the standard roles access in the base Drupal [5]. So, in this scenario, we theoretically have these potential access and rights combinations:
Since the group and user role categories can be quite extensive, the combinatorial result of these options can also be quite large.
Nonetheless, as a general proposition, these access and rights dimensions can capture most any reasonable use case.
One way to ease the management of these choices at the UI level is to create a series of access patterns or templates — called profiles — to which a newly registered dataset can be assigned. While the Drupal site owner could go in and change or tweak any of the individual assignments, the use of such profiles simplify the steps needed for the majority of newly registered datasets (Pareto assumption).
For instance, consider these possible profile patterns:
We can now expand this concept for a given dataset by adding the dimension of user type or category. Four categories of users can illustrate this user dimension:
(Of course, with a multitude of groups, there are potentially many more than four categories of users.)
To illustrate how we can collapse this combinatorial space into something more manageable, let’s look at what one of the profile cases noted above — that is the Public profile — can now be expressed as a pattern or template. In this example, the Public profile means that owners and some groups may curate the data, but everyone can see and access the data. Also note that export is a special case, which could warrant a sub-profile.
We also need to relate this Public profile to a specific dataset. For this dataset, we can characterize our “possible” assignments as described above as to whether a specific user category (O, G, R and P as noted above) has available a given function (open dot), gets permission rights to that function by virtue of the assigned profile (solid dot), or whether that function may also be limited to a specific group or groups (half-filled dot) or not.
Thus, we can now see this example profile matrix for the Public profile for an example dataset with respect to the available structWSF Web services:
Note, of course, that these options and categories and assignments are purely arbitrary for our illustrative discussion. Your own needs and circumstances may vary wildly from this example.
Matrices such as this seem complex, but that is why profiles can collapse and simplify the potential assignments into a manageable number of discrete options. The relevant question, with a quick answer, is for you to assemble profiles responsive to your own specific circumstances.
And, of course, if your pre-packaged profiles need to be tweaked or adjusted for a particular circumstance, the CMS enables all assignments to be accessed in individual detail.
Via this design, knowledge and collaboration networks can be deployed that support an unlimited number of configurations and options, all in a scalable, Web-accessible manner. The data that is accessed is automatically expressed as linked data. This same framework can be layered over in situ existing data assets to provide data federation and interoperable functionality, all responsive to standard enterprise concerns regarding data access, rights and permissions.
This is not science fiction, and this is not complex. When combined with its data mixing and conversion potentials [3], we can now see emerging a general framework that enables access and interoperability to virtually any data source and for virtually any purpose, with permissions and rights built in, anywhere and everywhere across the Web.
These are exciting prospects that were not possible until Web-oriented architectures with structured RDF data came to the fore. There are no longer any barriers to the powerful vision of complete data access and interoperability without disrupting existing assets.
And the mere thought of that, is, disruptive, indeed.
Interoperable Naïve Data Structs, Datasets and Canonical RDFAs I noted in my review of SemTech 2009, one of the key themes of the conference was data federation. Unfortunately, data federation has been a term a bit out of vogue for a while. (Though I still think it best captures the space.)
The current vernacular has been pushing forward an alternative: data mixing. One of the larger product pushes at the conference was by Zepheira for its new Freemix service and product. Freemix is a hosted service largely built around the Exhibit data display application, aided by some tools to make creating an exhibit easier. Exhibit is an attractive presentation system; for nearly three years AI3‘s own Sweet Tools dataset listing of semantic Web and -related tools has been presented via Exhibit.
Freemix looks promising and is now being offered in beta. But one thing caught my ear when listening to the company’s announcement: they are not yet able and ready to show the “data mixing” part of the system. Its release is apparently being delayed until later this year because of the difficulties encountered.
As a new term there is no “official” definition of data mixing. However, I think we can consider it as generally equivalent to the older data federation concept.
Data federation is the bringing together of data from heterogeneous and often physically distributed data sources into a single, coherent view. Sometimes this is the result of searching across multiple sources, in which case it is called federated search. But it is not limited to search. Data federation is a key concept in business intelligence and data warehousing and a driver behind master data management (MDM).
As I first wrote about data federation about five years ago [1]:
The Internet and its TCP/IP and Web HTTP protocols and XML standards in particular, have been major contributors to overcoming respective physical and syntactical and data exchange heterogeneities. The current challenge is to resolve differences in meaning, or semantics, between disparate data sources. Your “glad” may be someone else’s “happy” and you may organize the world into countries while others organize by regions or cultures.
Resolving semantic heterogeneities is also called semantic mediation or data mediation. Though it displays as a small portion of the pyramid above, resolving semantics is a complicated task and may involve structural conflicts (such as naming, generalization, aggregation), domain conflicts (such as schemas or units), or data conflicts (such as synonyms or missing values). Researchers have identified nearly 40 distinct types of possible semantic heterogeneities [2].
Ontologies provide a means to define and describe these different worldviews. Referentially integral languages such as RDF (Resource Description Framework) and its schema implementation (RDF-S) or the Web ontological description language OWL are leading standards among other emerging ones for machine-readable means to communicate the semantics of data.
Fortunately, we have climbed most of this data federation pyramid. The stumbling block now are the semantics. This is made all the harder when we place too much burden on the data transmission or “packet” itself. In other words, does exchange also carry with it the burden of meaning? The rest of this post tries to explain what I mean by this and how it relates to our new structWSF Web services framework.
Not to pick on any one thing or any individuals, but three recent threads on semantic Web-related mailing lists help illustrate in various ways some interesting mindsets. While there is much on each of these threads of other value, I’m only focusing on a narrow topic from each based on my thesis at hand.
And, what is that thesis? It is simply that we too often mix instance record and attribute assertions with schema representations and world views. And, when we do, we sometimes make mountains out of molehills (or mix apples and oranges to completely mix metaphors).
JSON (JavaScript Object Notation) is a data notation or syntax, easily created and widely used for current Web apps. It has a rather simple syntax for representing attribute-value pairs. Many useful tools and parsers for the serialization exist.
In keeping with his general and broad criticisms of how the semantic Web standards and approaches have been promulgated by the W3C to date, John Sowa most recently expressed his ideas in a posting to the ontolog-forum mailing list under the heading of ‘Semantic Systems’ [3]. In this thread, John proposes:
1. The recommended exchange form for RDF will become JSON. Any JSON documents that are limited to triples can use the old XML-based RDF form, but they can also use the more compact and more general full JSON.
Then, in a subsequent posting to that thread he notes:
5. The W3C made a major blunder with a one-size-fits-all approach that tried to use a document tagging language as a knowledge representation language. The result was the *worst* notation for logic ever invented.
Finally, he goes on to note in a further post:
JSON could be used as an alternative to XML for the syntax, but the lack of a standard semantics for JSON means that it could *not* be used as a replacement for RDF *unless* an official standard were adopted for mapping RDF to and from a particular subset of JSON whose semantics was defined in Common Logic.
All of this John proposes in the spirit of:
The goal of my proposal is nothing less than a total *integration* of the Semantic Web methodologies with the methodologies that have been used in the traditional software development community [3].
I find common ground with a couple of the ideas in this proposal. First, accepted formats like JSON should have a prominent place in data exchange. Second, leveraging methodologies used in the traditional community is definitely a good thing.
But John, while suggesting reuse of existing traditions, is also paradoxically recommending a wholesale replacement for RDF. He is also positing a single exchange standard (JSON). And, he stops tantalizingly short of recognizing an important truth that I’m sure he knows: simple instance record assertions and representations — the essence of data exchange — can and should be viewed separately from schema representations.
As I have noted in my earlier naïve data ‘structs’ series, there are in fact scores of existing data transfer formats that have been adopted by their communities — and are likely to remain popular within those communities for some time — that can play a similar role to JSON. So long as the role of data exchange is kept to the assertions (“metadata”) about instances, many formats can play in the sandbox.
The role of RDF may or may not reside with data exchange. To conflate and equate RDF and JSON is to reduce the power of keeping instance record representations separate from schema and world view representations. John’s basic sensibilities, I think, could be more effectively promoted by not posing ‘either-or’ strawmen and recognizing that data exchange formats will ALWAYS be diverse and heterogeneous.
Observation: Existing and emerging data ‘structs’ useful to data exchange will remain manifest in format and diversity; data exchange imperatives are a different matter from schema and knowledge representation.
Somewhat in contrast to this thread was a different one by Martin Hepp, editor of the excellent Good Relations ontology, on the LOD (linked open data) mailing list [4]. This thread, which sensibly questions how difficult it is for mere mortals to configure an Apache server to support publishing RDF, reached further into the realm of RDFa as a document annotation language.
As Hepp states,
The reason is that, as beautiful the idea is of using RDFa to make a) the human-readable presentation and b) the machine-readable meta-data link to the same literals, the problematic is it in reality once the structure of a) and b) are very different. For very simple property-value pairs, embedding RDFa markup is no problem. But if you have a bit more complexity at the conceptual level and in particular if there are significant differences to the structure of the presentation (e.g. in terms of granularity, ordering of elements, etc.), it gets very, very messy and hard to maintain.
Further discussion in this thread elaborates the interest in having the documents in which the RDFa is embedded carry much more schema-level information.
Like the Sowa case, this raises the question of where to draw the line. Should embedded metadata in documents carry complex schema information as well? So, we now shift the focus from data exchange to schema representation.
I think this is really unnecessary since it is quite easy in RDFa to refer to a separately specified schema. By, in this case, conflating metadata transfer and exchange with schema, the bar has been raised unnecessarily high.
If we need to capture schema and world views, fine, let us do so directly and succinctly. Then, let our document metadata (in this case using RDFa) make attribute assertions about that “payload” simply and cleanly. The Web certainly does not need individual documents carrying with them entire schema representational views of the world.
Observation: Data exchange, even based on RDF (via RDFa), is best kept to the assertions of facts and attributes.
In a microformats context, Thomas Loertsch posed some questions on mixing vocabularies [5] and how they should be interpreted. This caused an involved discussion of intent and possible implications and best practices, with discussants including Brian Suda, Peter Mika, Ben Ward and others. It also led to the start of a useful wiki page on how objects should be represented in Web pages when multiple microformats can be invoked.
For quite some time microformats, I think, have gotten the “mix” just about right. They have created well-reasoned attributes for distinct instance types and seek to keep their embedding of that information simple in existing documents. Some advocate while others question the rigor of the microformat structure; that is not the topic here.
What is interesting about this thread is that it evolved to discuss the implications and best practices when an author posts a document with more than one microformat. How do these vocabularies relate? How should we, as “consumers” of the document, parse the vocabularies?
Yahoo!’s SearchMonkey service has recognized microformats for some time, and its questions regarding interpretation and best practices in the thread were natural. But the interesting point that seemed to come out of this thread is that users will post microformats as they wish. While care and standards in the design of the microformats can help reduce confusion and conflict, it can not guarantee it. The final responsibility for proper ingest and processing likely resides with the aggregators and publishers that consume such data.
So, here, too, we have another case of asserting metadata and embedding for data exchange in a slightly different native format than RDF. Huzzah!
Observation: Standards setters and consuming agents (often aggregators, publishers or search engines) should take lead responsibility for best practices and processing attribute data, realizing that original authors and developers may not fully comply.
These examples are a bit of a long way around the barn to reinforce what we have been arguing for some time: the need for a proper split between the ABox (assertions related to instances) and the TBox (concept relationships, schema and world views) [6]. This has been a pretty constant theme in our writing, ranging from first introductions, to its relation to description logics, relationships to existing data ‘structs’, and explicit discussion of ABox and TBox roles in a four–part series.
One of the key points throughout this writing is that an ABox-TBox mindset provides a context and rigor for looking at questions such as our three examples above. In all three cases, I argue, the seeming conundrums result from lacking this mindset. Once this mindset is applied, the respective roles of various data formats, RDF, schema and the like naturally fall into place.
Of course, the Web is also a dirty and chaotic place where niceties of design and best practices are routinely ignored or unknown or purposefully rejected. So be it. This is reality. This reality needs to be accommodated. But good design can help overcome it and work to establish resilient, flexible architectures.
Of course, even though this might be good design, there is no ability to enforce such distinctions across the Web. However, insofar as key implementors are concerned (standards writers, major publishers, tools developers, industry experts, and the like) we can put in place better approaches. This mantra is at the heart of all that Structured Dynamics does — including the structWSF Web services framework, just released as open source code.
So, now we can finally turn our attention to the structWSF Web services framework, more broadly described here.
There are a number of perspectives and contexts to view this structWSF framework. In this posting, we take the boundary conditions of data formats and data exchange [7]. The key question for this perspective is: given the realities noted above, what is an adaptive framework for data mixing on the Web? Our schematic answer to this question is below:
The basic design has two key data considerations. First, all structWSF tools and Web services and schema work from the canonical RDF data model. It is the hub and common denominator for all structWSF installations. We are able to design and optimize generic tools and services (including converters) around this canonical framework.
Second, we assume most everything in the outside world to be non-compliant with this canonical model, with the data representations often naïve and incomplete. Converters (also known as translators or RDFizers) are an essential bridge to this external world, and need to be designed for re-use and extensibility.
Where the outside world is compliant, they conform to the structWSF APIs or are themselves structWSF installations. In these cases, direct data exchange and access with permission rights occurs at a dataset level (not shown).
Converters are themselves bona fide Web services at the structWSF level. (Only a few are presently included in the alpha release.) While some may be one-off converters (sometimes off-the-shelf RDFizers), and often devoted to large volume external data sources, it is also helpful to emphasize one or more “standard” naïve external formats. A “standard” external format allows for a more sophisticated converter and enables specific tools to be more easily justified around the standard naïve format.
As noted above, this “standard” is often JSON or a derivative of JSON. But, just as readily, the common ‘naïve’ format could be SQL from relational databases or another format common to the community at hand. In many ways, because the emphasis of data exchange is on the ABox and instance records and assertions (and attribute extensions), the actual format and serialization is pretty much immaterial.
Emphasizing one or a few naïve external formats allows more tools and services to be cost-effectively developed for those formats. And, even though the format(s) chosen for this external standard may lack the expressiveness of RDF (and, ultimately, OWL), because the burden is principally related to data exchange, this layer can be readily optimized for the deployment at hand.
Besides import converters it is also important to have export services for the more broadly used naïve external formats. In fact, some structWSF services can be devoted to data cleanup or attribute (property) or object reconciliation (including disambiguation as a possibility). In this manner, structWSF installations could also improve the authority and trustworthiness of standard data in the wild.
Another common service for this naïve data is to give it unique URI identifiers and to make it Web-accessible, thus turning it into linked data.
Such generic services are possible because the “highest common denominator” for the system is the canonical RDF model. Because it is the consistent basis for tools and services, once a converter is available and the external information schema is mapped to the internal structure, all existing tools and services are available for re-use. Moreover, this system and its datasets are now ready for sharing with other structWSF instances, within the enterprise or beyond.
Thus, we begin to see a network of canonical “hubs” in a sea of heterogeneity, the interoperation of which is facilitated by a structWSF framework at every network node. This design is discussed more in the next part of this series.
Some, such as Sowa noted above, would prefer a grounding in common logic (CL) as opposed to RDF. Our choice to use RDF is based on the simplicity and understandability of the data model, plus the richness of languages and standards from the W3C that surround the framework.
Even here, however, the RDF basis of structWSF need not be the final word. Because of a keen intent to keep all designs and ontologies used by structWSF firmly grounded in description logics, it is possible for the structWSF basis to be converted to other languages and frameworks such as CL that can be expressed in DL.
Data mixing — or more preferably, data federation — has as its heart the premise of heterogeneous and distributed data sources. It implicitly acknowledges differences in syntax, semantics and serializations.
The design and architecture of structWSF is similarly premised. While each of us may prefer one model or one format over others, we must interoperate in the real world. And that world, for many understandable and immutable reasons, will retain its diversity. Accepting this reality is a first step to adaptive design.
So, we control what we can control, and we adapt to what else exists. We have chosen RDF as the canonical data model that we can control and have embedded it in a Web services framework that is Web-based and scalable; in other words, a fully compliant Web-oriented architecture. These are the conceptual foundations to structWSF.
To be sure, structWSF in its current alpha release is quite raw in many areas and incomplete in others. But we will continue to work on it — and invite your participation to do the same — such that it can fulfill its destiny as a data federation framework for the Web.
[6] This is our working definition of the ABox and TBox in specific reference to description logics:
The slides for our conStruct announcement at SemTech 2009 have been posted on Slideshare.
The slideshow, Data-driven Apps with conStruct, have much on the architecture and benefits of conStruct, from the context of the Bibliographic Knowledge Network (BKN) project. The slides came from my talk on “BKN: Building Knowledge through Communities, and Communities through Knowledge.”
Enjoy!
OK, I admit it. I’m a dweeb and a suck-up. I have just returned from the Semantic Technology Conference in San Jose (CA) and I could not be more impressed. For real semantic Web action, this was the place to be. And, I’m sure, it will continue to be so if its leadership stays intact for some time to come.
I know, it is really not my style to applaud others when I could be patting myself on the back. But, hey, this was such a remarkable meeting in so many ways that I feel I have to break from precedent.
In terms of dislcosure let me be clear: I hope I get invited back to speak again next year (only this time in a bigger forum. Hint. Hint.) But, even if not invited to speak, me and my company will be there with bells on for this simple reason: this is the semantic Web confab that matters.
Unlike others that have noted specific talks, etc., I will not do so. Not because there were not talks that warrant such visibility; indeed, there were a tremendous number. My biggest regret of the meeting is that I could only taste a portion of all of the talks because so much was going on. But, rather than singling out any specific talks, I’d like to comment more thematically.
The conference organizers reported that more than 500 paper suggestions were put forward for what ended up being about 150 speaking slots. This was in addition to many tutorials, keynotes and many rump meetups. My best guess is that there were on the order of 1200 in total attendance. It was a packed five days or so. San Jose and the facilities were excellent.
When tech shows reportedly are down on average 40% or so from prior years, it is pretty remarkable to have one exceed its prior attendance, which SemTech 2009 apparently did. There were real customers, real use cases and real interest at every turn and in every conversation. Many hard problems were brought forward; some without acceptable solutions yet.
The real theme I kept hearing was: data federation, data federation, data federation. The potential for semantic Web technologies via the RDF data model and OWL and ontologies for finally breaking down the barriers between data silos was hammered and probed. The timing, I think, could not have been better than to have received shortly before the conference the timely PricewaterhouseCoopers technology quarterly report on linked data in the enterprise that I reviewed a couple of weeks back.
I think we can safely say that the advances from linked data in the past couple of years have been huge enablers and eye-openers to these prospects. But I also had the sense that the discourse is now moving beyond linked data as practiced so far. Web identifiers and Web access, I think, have won the argument. It is now time to move on to real data, interoperability and efficient tools and build-out.
To be sure there were discussions of more consumer-oriented apps and search. But the major energy and action seemed to center on the enterprise.
The idea is how can RDF bring us leverage, not replace what already exists. After 30 years of frustration, how can we finally solve the data federation problem? How can we remove the historical brittleness of applications and report writers? How can we actually begin to extract business intelligence from the massive data assets we already have at hand?
Asking enterprises to junk what they have for promises and prayers will not cut it. The winning strategy, and the challenge I kept hearing was: How can we layer on semantic technologies and RDF to bridge our existing data stores? How can we leave our RDBMs in place while gaining the goodness of ontologies and semantics?
We clearly see all around us the power of open source and the withering of proprietary apps and approaches. But, much data and information will remain private and needs to have access and rights restrictions. What answers does semantic technologies offer in these areas?
Then, as suppliers in this brave new world of open source and low software rents, what is the winning business model? Tom Tague helped articulate the importance of revenue models and options in his keynote; it resonated with already ongoing discussions in the hallways.
I’ve been in this space for more years than I care to admit. My observation from prior years is that some new “big thing” is identified, given blessing and push by the industry analysts (always with a new acronym), and then hyped like hell. Maybe it is the current challenged economic climate, but it feels like those days are over. For good.
Hype will not open wallets anymore. Case studies and real warts will help bring confidence that something truly different is at hand with semantic technologies. Our central challenge as suppliers to this market is to respond to today’s pragmatic imperatives. We must demonstrate more with less and faster. We must emphasize leverage and re-use. We must respect the trillions in already sunk IT assets.
I think this matters much for three different communities.
For enterprises, I think it means that it is time for pilots and engagements. Both the market and the suppliers can not move this space forward rapidly without meaningful engagement. We’re ready, and it seems like many of you are as well. Push it with your bosses; we’ll deliver.
For the linked data community, where do you go next? I, too, heard some of the criticisms about too much “ontology.” But such discussion risks wasting the gains already achieved. If we do not listen and respond to the market’s imperatives and voice, we will become irrelevant. Let’s accept linked data as a tremendously helpful step in an ongoing progression, but continue to mature.
For some of the more established semantic technology providers, we have to make it simpler and faster. Expensive ontology development, too, will become irrelevant if we are indeed going to replace conventional software development with data-driven apps. Fortunately, I saw much, much exciting in this space and really had my eyes opened to tremendous innovation.
Outside of the venue, I heard from some of my prior Silicon Valley colleagues that this was the most constrained VC situation they have seen in decades. Funds may exist, but capital calls are not being made. What little powder there is, is being kept dry to triage existing investments. It is a good thing capital requirements for new start-ups have declined so much in recent years, because VCs are unlikely to fund the gap. And, aside from some big, prominent initiatives like data.gov or health care digitization, most savvy observers would bet that US and EU funding will also begin drying up in the coming years.
All of this can sound like bad news, but I think it is an opportunity: As technologists and suppliers, we must be relentlessly revenue focused and deliver what the market is demanding: more with less faster while preserving existing investments.
The organizers from Wilshire Conferences and their entire staff did an absolutely tremendous job. Tony Shaw, Eric Franzon, Steve Bastasini and Eric Hoffer (I know Eric, you were only pinch hitting), plus the many on-site staff, were uniformly professional and unobtrusive. Sally Khudairi on PR and the A/V and registration crew were also excellent.
I once had responsibility for an annual technical meeting that averaged more than 2000 attendees and 150 exhibitors and I appreciate how many moving parts there are behind the scenes. Things work when nothing gets noticed. My guess is few noticed any issues or problems at this conference.
The stated aim on the intro slide to each session was to educate, and the agenda certainly achieved that. A/V was professional; time was kept; coffee did not run out; wi-fi glitches were quickly solved.
Sure, like any business, there is some pay-to-play in such conferences. Big sponsors get more slots and visibility. This reality, however, was also well balanced with new voices and innovative presenters. My “to do” notes and contacts resulting from the conference will take quite some time to work through.
One of the things I really appreciated was how the time slots and composition of talks and activities were varied each day. I have not attended a meeting before that did such a good job of mixing the schedule up to keep things feeling fresh over so many intense days.
Much, thanks, folks, for a conference exceedingly well done.
If I had to note a quibble I guess it would be to start the conference with more challenges and innovations. While the tutorials are very helpful, the first opening talks, I hope, could not be quite so introductory in nature. I think things are maturing fast. But, I could be wrong. First-time attendees from the marketplace should probably guide how such events start ramping up the engine.
As I noted, I and Structured Dynamics will be back next year, and hopefully contribute in more ways as well. The venue for SemTech 2010 has changed to the Hilton in San Francisco on June 21 – 25.
So, start saving for your travel budget now. This is “must see” semantic Web. And I look forward to seeing you there in a year!