Posted:February 13, 2006

Conventional service-oriented architectures (SOAs) have been found to have:

  • Slow and inefficient bindings
  • Complete duplication of information processing in requests because of no caching, and
  • Generally slow performance because of RDBMS storage.

These problems are especially acute at scale.

Frank Cohen recently posted a paper on IBM’s developerWorks, "FastSOA: Accelerate SOA with XML, XQuery, and native XML database technology: The role of a mid-tier SOA cache architecture," that presents some interesting alternatives to this conundrum.

The specific FastSOA proposal may or may not be your preferred solution if you are working with complex SOA environments at scale. But the general overview of conventional SOA constraints (in the SOAP framework) is very helpful and highly recommended.

Posted by AI3's author, Mike Bergman Posted on February 13, 2006 at 9:59 am in Information Automation, Semantic Web | Comments (0)
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Posted:February 1, 2006

IBM has announced it has completed the first step of making the Unstructured Information Management Architecture (UIMA) available to the open source community by publishing the UIMA source code to SourceForge.net. UIMA is an open software framework to aid the creation, development and deployment of technologies for unstructured content. IBM first unveiled UIMA in December of 2004. The source code for the IBM reference implementation of UIMA is currently available and can be downloaded from http://uima-framework.sourceforge.net/ . In addition, the IBM UIMA SDK, with additional facilities and components, can be downloaded for free from http://www.alphaworks.ibm.com/tech/uima .

UIMA has received support from the Defense Advanced Research Projects Agency (DARPA) and is currently in use as part of DARPA’s new human language technology research and development program called GALE (Global Autonomous Language Exploitation). UIMA is also embedded in various IBM products for processing unstructured information.

Posted:November 23, 2005

In earlier posts I have put forward a vision for the semantic Web in the enterprise that has an extensible database supporting semi-structured data at its core with XML mediating multiple ingest feeds, interaction with analytic tools, and sending results to visualization and reporting tools.

This is well and good as far as it goes.  However, inevitably, whenever more than one tool or semi-structured dataset is added to a system, it brings with it a different “view” of the world.  Formalized and standardized protocols and languages are needed to both:  1) capture these disparate “views” and 2) provide facilities to map them to resolve data and schema federation heterogeneities.  These are the roles of RDF and OWL.

Fortunately, there is a very active community with tools and insights for working in RDF and OWL.  Stanford and UMBC are perhaps the two leading centers of academic excellence.

If you are not generally familiar with this stuff, I recommend you begin with the recent “Order from Chaos” from Natalya Noy of the Protégé group at Stanford Medical.  This piece describes issues like trust, etc., that are likely not as relevant to application of the semantic Web to enterprise intranets as they are to the cowboy nature of the broader Internet.  However, much else of this article is of general use to the architect considering enterprise applications.

To keep things simple and to promote interoperability, a critical aspect of any enterprise semantic Web implementation  will be providing the “data API” (including extensible XML, and RDF and OWL) standards that govern the rules of how to play in the sandbox.  Time spent defining these rules of engagement will pay off in spades in relation to any other appproach for multiple ingest, multiple analytic tools and multiple audiences, reports and collaboration.

Another advantage of this approach is the existence of many open source tools for managing such schema (e.g.Protégé) and visualization (literally dozens), among thousands of ontologies and other intellectual property.

Posted:November 15, 2005

Today, the value of the information contained within documents created each year in the United States represents about a third of total gross domestic product, or an amount of about $3.3 trillion.[1] Moreover, about $800 billion of these expenditures are wasted and are readily recoverable by businesses, but are not. Up to 80% of all corporate information is contained within documents. Perhaps up to 35% of all company employees in the U.S. can be classified as knowledge workers using and relying on documents. So, given these factors, how could such large potential cost savings from better document use be overlooked?

Previous installments in this series have looked at issues of private v. public information, barriers to collaboration, and solutions as being too expensive as possible reasons for why these potential savings are not realized. This fourth installment looks at a fourth reason; namely, what might be called issues of attention, perception or psychology. Interesting observations in this area come from disciplines as diverse as sales, behaviorial psychology, economics and operations research.

The SPIN Rationale

One explanation for this lack of attention can be described by the fact that document problems are still in the area of implicit needs as opposed to explicit needs. In other words, the perception of the problem is still situational but has not yet become concrete in terms of bottom-line impacts.

In Neil Rackham’s SPIN sales terminology (Situation Problems Implications Needs/pay-off),[2] the enterprise document market is still at a “situational” level of understanding. Decisions to buy or implement solutions are largely strategic and limited to early adopters that are the visionaries in their market segments. The inability to express and quantify the implications of not realizing the value of document assets means that ROI analysis can not justify a deployment and market growth can not cross the chasm.

The situation begins with the inability to quantify the importance of both internal and external document assets to all aspects of the enterprise’s bottom line. Early adopters of enterprise content software typically capture less than 1% of valuable internal documents available; large enterprises are witnessing the proliferation of internal and external Web sites, sometimes exceeding thousands; use of external content is presently limited to Internet search engines, producing non-persistent results and no capture of the investment in discovery or results; and “deep” content in searchable databases, which is common to large organizations and represents 90% of external Internet content, is completely untapped. Indeed, the issue of poor document use in an organizaation can be seen in terms of the figure below:

The diagram indicates that these root conditions or situations cause problems in low quality of decisions or low staff productivity. For examples, documents or proposals get duplicated without knowledge of prior effort that could be leveraged; opportunities are missed; or outdated or incomplete information is applied to various tasks. These root problems can impact virtually all aspects of the organization’s operations: sales are lost; competitors are overlooked; compliance requirements are missed. These problems can lead to significant bottom-line implications from revenue and market share, to reputation and valuation and even indeed survival.

Thus, in the view of the SPIN model, the lack of attention to the issue of document assets can, in part, be ascribed to the sales or investigatory process. Specific questions have not been posed that move the decision maker from a position of situational awareness to one of explicit bottom-line implications.

There is undoubtedly truth to this observation. Sales of large document solutions to enterprises require a consultative sales approach and significant education of the market is required. As a first-order circumstance, this implies long sales leadtimes and the dreaded “educating the market” that most VCs try to avoid.

But there are even larger factors at play than a lack of explicitness regarding document assets.

The Ubiquitous and Obvious Are Often Overlooked

Put your index finger one inch from your nose. That is how close  — and unfocused — document importance is to an organization. Documents are the salient reality of a knowledge economy, but like your finger, documents are often too close, ubiquitous and commonplace to appreciate.

The dismissal of the ubiquitous, common or obvious can be seen in a number of areas. In terms of R&D and science, this issue has been termed “mundane science” wherein most academic research topics exclude many of the issues that affect the largest number of people or have the most commonality. [3] In organizational and systems research, such issues have also been the focus of better, more rigorous problem identificaton and analysis techniques such as the “rational model” or the “theory of constraints” (TOC).[4]

Compounding the issue of the overlooked obvious is the lack of a quantified understanding of the problem. There is an old Chinese saying that roughly translated is “what cannot be measured, cannot be improved.” Many corporate executives surely believe this to be the case for document creation and productivity.

More Specifically: Bounded Awareness

Chugh and Bazerman have recently coined a term “bounded awareness” for the phenomenon of missing easily observed and relevant data.[5] As they explain:

“Bounded awareness is a phenomenon that encompasses a variety of psychological processes, all of which lead to the same error: a failure to see, seek, use, or share important and relevant information that is easily seen, sought, used, or shared.”

The authors note the experiments from Simons[6] that extend Neisser’s 1979 video in which a person in a gorilla costume walks through a basketball game, thumping his chest, and is clearly and comically visible for more than five seconds, but is not generally recalled by observers without prompting.

Chugh and Bazerman classify a number of these phenomena, with two most applicable to the document assets problem:

  • Inattentional blindness — direct information when attention is drawn or focused elsewhere
  • System neglect — this phenomenom is the tendency to undervalue a broader, pivotal factor to subsidiary ones, as in for example the effect of campaign finance-reform on specific political issues. In the document assets case, the general role of document access and management is neglected as a system over more readily understood specific issues such as search or spell checking. In other words, people tend to value issues that are more clearly seen as end states or outcomes.

Note the relation of these studies by behaviorial psychologists to the SPIN terminology of the sales executive. Clearly, perceptual studies by scientists will lead to better understandings of market outreach.

Perceptions of Intractability?

An earlier installment in this series noted the high cost of enterprise content solutions, more generally linked to software that performed poorly and did not scale. In computer science, intractable problems are those which take too long to execute, the problem may not be computable, or we may not know how to solve the problem (e.g., problems in artificial intelligence). Tractable problems can run in a reasonable amount of time for even very large amounts of input data. Intractable problems require huge amounts of time for even modest input sizes.[7]

At low scales, the efficiency of various computer algorithms is not terrible important because multiple methods can produce acceptable performance times. But at large scales whether a problem is tractable or not is not fixed: it depends critically on the efficiency of the algorithm applied to the problem. Let’s take for example the issue of searching text items:

Take n to represent the number of keys in a list, and let O represent the order of the number of comparison operations required to find an entry. For a small number of n items, the algorithm used is unimportant, and even a slow sequential search will work well. Sequentially searching the list until the desired match is found is O (n), or linear time. If there are 1000 items in a list, and there is an equal probability of searching for any item in the list, on average it will require n/2 = 500 comparisons to find the item (assuming all items already are on the list). A binary search works by dividing the list in half after each comparison. This is logarithmic time O (log n ), much faster than linear time. For a 1000 item example it works out to about 10 comparisons. An O (1) operation, such as hashing, is applicable when some algorithm computes the item location and then retrieves it. On large lists it will significantly outperform a binary search, because it makes no comparisons. (It is a little more complicated than that because there may be collisions for the same address computed for different keys.) However, if the location is already known, even the hashing computation is unnecessary. This is what happens with direct addressing (the technique used by BrightPlanet), which will obtain the desired item in a single step.[8]

Poorly performing algorithms at large scales can require processing times for updates that take longer than the period between updates, and, thus, at least for that algorithm, are intractable at those scales.

This is one of the key and perceived problems to most document processing software at large scales — their computational inefficiencies do not allow updates to occur for the meaningful document volumes important to larger organizations. Whether the specific reasons are known by company managers and IT personnel, it is a widespread understanding — correct for most vendors — within the marketplace.

Since BrightPlanet‘s core index work engine is more efficient than other approaches (due, in part, to better sorting mechanisms as noted above, but also due to other factors), current perceived limits of intractability may not apply. However, these advances are still not generally known. Until broader understanding for how more contemporary approaches to document use and management are gained, perceptions of past poor performance will limit market acceptance.

Educating the Market

Thus, factors of awareness, attention and perception are also limiting the embrace of meaningful approaches to improve document access and use and achieve meaningful cost savings. These challenges may mean that the document intelligence and document information automation markets still fall within the category of needing to “educate the market.” Since this category is generally dreaded by most venture capitalists (VCs), that perception is also acting to limit the achievable improvements and cost savings available to this market.

But there is perhaps a very important broader question that remains open here: educating the market through the individual customer (viz. the SPIN sale) vs. educating the market through breaking market-wide bounded awareness. In fact the latter, much as what occurred with data warehousing 15-20 years ago, can create entirely new markets. This latter category should perhaps be of much greater VC interest with its accompanying potential for first-mover advantage.


[1] Michael K. Bergman, “Untapped Assets: The $3 Trillion Value of U.S. Enterprise Documents,” BrightPlanet Corporation White Paper, July 2005, 42 pp. All 80 references, 150 citations and calculations are fully documented in the full paper. See http://www.brightplanet.com/technology/whitepapers.asp.

[2] Neil Rackham, SPIN Selling, McGraw Hill, 197 pp., 1988.

[3] Daniel M. Kammen and Michael R. Dove, “The Virtues of Mundane Science,” Environment, Vol. 39 No. 6, July/August 1997. See http://ist-socrates.berkeley.edu/~rael/Mundane_Science.pdf

[4] Victoria Mabin, “Goldratt’s ‘Theory of Constraints’ Thinking Processes: A Systems Methodology linking Soft with Hard,” The 17th International Conference of The System Dynamics Society and the 5th Australian & New Zealand Systems Conference, July 20 – 23, 1999, Wellington, New Zealand, 12 pp. See http://www.systemdynamics.org/conf1999/PAPERS/PARA104.PDF

[5] Dolly Chugh and Max Bazerman, “Bounded Awareness: What You Fail to See Can Hurt You,” Harvard Business School Working Paper #05-037, 35 pp., August 25, 2005 revision. See http://www.people.hbs.edu/mbazerman/Papers/05-037.pdf

[6] See the various demos available at http://viscog.beckman.uiuc.edu/djs_lab/demos.html.

[7] Professor Constance Royden, College of the Holy Cross, course uutline for CSCI 150, Tractable and Intractable Problems, Spring 2003. See http://mathcs.holycross.edu/~croyden/csci150spr03/notes/lec33_tractable.html

[8] R. L. Kruse, Data Structures and Program Design, Prentice Hall Press, Englewood Cliffs, New Jersey, 1987.

NOTE: This posting is part of a series looking at why document assets are so poorly utilized within enterprises.  The magnitude of this problem was first documented in a BrightPlanet white paper by the author titled, Untapped Assets:  The $3 Trillion Value of U.S. Enterprise Documents.  An open question in that paper was why more than $800 billion per year in the U.S. alone is wasted and available for improvements, but enterprise expenditures to address this problem remain comparatively small and with flat growth in comparison to the rate of document production.  This series is investigating the various technology, people, and process reasons for the lack of attention to this problem.

Posted by AI3's author, Mike Bergman Posted on November 15, 2005 at 11:55 am in Adaptive Information, Document Assets, Information Automation | Comments (2)
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Posted:November 1, 2005

The first recorded mentions of “semi-structured data” occurred in two academic papers from Quass et al.[1] and Tresch et al.[2] in 1995. However, the real popularization of the term “semi-strucutred data” occurred through the seminal 1997 papers from Abiteboul, “Querying semi-structured data,” [3] and Buneman, “Semistructured data.” [4] Of course, semi-structured data had existed well before this time, only it had not been named as such.

What is Semi-structured Data?

Peter Wood, a professor of computer science at Birkbeck College at the University of London, provides succinct definitions of the “structure” of various types of data:[5]

  • Structured Data — or classes. Entities in the same group have the same descriptions (or attributes), while descriptions for all entities in a group (or schema): a) have the same defined format; b) have a predefined length; c) are all present; and d) follow the same order. Structured data are what is normally associated with conventional databases such as relational transactional ones where information is organized into rows and columns within tables. Spreadsheets are another example. Nearly all understood database management systems (DBMS) are designed for structural data
  • Unstructured Data — in this form, data can be of any type and do not necessarily follow any format or sequence, do not follow any rules, are not predictable, and can generally be described as “free form.” Examples of unstructured data include text, images, video or sound (the latter two also known as “streaming media”). Generally, “search engines” are used for retrieval of unstructured data via querying on keywords or tokens that are indexed at time of the data ingest, and
  • Semi-structured Data — the idea of semi-structured data predates XML but not HTML (with the actual genesis better associated with SGML, see below). Semi-structured data are intermediate between the two forms above wherein “tags” or “structure” are associated or embedded within unstructured data. Semi-structured data are organized in semantic entities, similar entities are grouped together, entities in the same group may not have same attributes, the order of attributes is not necessarily important, not all attributes may be required, and the size or type of same attributes in a group may differ. To be organized and searched, semi-structured data should be provided electronically from database systems, file systems (e.g., bibliographic data, Web data) or via data exchange formats (e.g., EDI, scientific data, XML).

Unlike structured or unstructured data, there is no accepted database engine specific to semi-structured data. Some systems attempt to use relational DBMS approaches from the structured end of the spectrum; other systems attempt to add some structure to standard unstructured search engines. (This topic is discussed in a later section.)

Semi-structured data models are sometimes called “self-describing” (or schema-less). These data models are often represented as labelled graphs, or sometimes labelled trees with the data stored at the leaves. The schema information is contained in the edge labels of the graph. Semi-structured representations also lend themselves well to data exchange or the integration of heterogeneous data sources.

A nice description by David Loshin[6] on Simple Semi-structured Data notes that structured data can be easily modeled, organized, formed and formatted in ways that are easy for us to manipulate and manage. In contrast, though we are all familiar with the unstructured text in documents, such as articles, slide presentations or the message components of emails, its lack of structure prevents the advantages of structured data management. Loshin goes on to describe the intermediate nature of semi-structured data:

There [are] sets of data in which there is some implicit structure that is generally followed, but not enough of a regular structure to “qualify” for the kinds of management and automation usually applied to structured data. We are bombarded by semi-structured data on a daily basis, both in technical and non-technical environments. For example, web pages follow certain typical forms, and content embedded within HTML often have some degree of metadata within the tags. This automatically implies certain details about the data being presented. A non-technical example would be traffic signs posted along highways. While different areas use their own local protocols, you will probably figure out which exit is yours after reviewing a few highway signs.

This is what makes semi-structured data interesting–while there is no strict formatting rule, there is enough regularity that some interesting information can be extracted. Often, the interesting knowledge involves entity identification and entity relationships. For example, consider this piece of semi-structured text (adapted from a real example):

John A. Smith of Salem, MA died Friday at Deaconess Medical Center in Boston after a bout with cancer. He was 67.

Born in Revere, he was raised and educated in Salem, MA. He was a member of St. Mary’s Church in Salem, MA, and is survived by his wife, Jane N., and two children, John A., Jr., and Lily C., both of Winchester, MA.

A memorial service will be held at 10:00 AM at St. Mary’s Church in Salem.

This death notice contains a great deal of information–names of people, names of places, relationships between people, affiliations between people and places, affiliations between people and organizations and timing of events related to those people. Realize that not only is this death notice much like others from the same newspaper, but that it is reasonably similar to death notices in any newspaper in the US.

Note in Loshin’s example that the “structure” added to the unstructured text (shown in yellow; my emphasis) to make this “semi-structured” data arises from adding informational attributes that further elaborate or describe the document. These attributes can be automatically found using “entity extraction” tools or similar information extraction (IE) techniques, or manually identified. [7] These attributes can be assigned to pre-defined record types for manipulation separate from a full-text seach of the document text. Generally, when such attributes are added to the core unstructured data it is done through “metatags” that a parser can structurally recognize, such as by using the common open and close angle brackets. For example:

<author=John Smith>

In semi-structured HTML, the tags that provide the semi-structure serve a different purpose in terms of either formatting instructions to a browser or providing reference links to internal anchors or external documents or pages. Note that HTML also uses the open and close angle brackets as the convention to convey the structural information in the document.

The Birth of the Semi-structured Data Construct

One could argue that the emergence of the “semi-structured data” construct arose from the confluence of a number of factors:

  • The emergence of the Web
  • The desire for extremely flexible formats for data exchange between disparate databases (and therefore useful for data federation)
  • The usefulness of expressing structured data in a semi-structured way for the purposes of browsing
  • The growth of certain scientific databases, especially in biology (esp., ACeDB), where annotations, attribute extensibility resulting from new discoveries, or a broader mix of structural and text data was desired.[8]

These issues first arose and received serious computer science study in the late 1970s and early 1980s. In the early years of trying to find standards and conventions for representing semi-structured data (though not yet called that), the major emphasis was on data transfer protocols.

In the financial realm, one proposed standard was electronic data interchange (EDI). In science, there were literally tens of exchange forms proposed with varying degrees of acceptance, notably abstract syntax notation (ASN.1), TeX (a typesetting system created by Donald Knuth and its variants such as LaTeX), hierarchical data format (HDF), CDF (common data format), and the like, as well as commercial formats such as Postscript, PDF (portable document format), RTF (rich text format), and the like.

One of these proposed standards was the “standard generalized markup language” (SGML), first published in 1986. SGML was flexible enough to represent either formatting or data exchange. However, with its flexibility came complexity. Only when two simpler forms arose, namely HTML (HyperText Markup Language) for describing Web pages and XML (eXtensible Markup Language) for data exchange, did variants of the SGML form emerge as widely used common standards.[9]

The XML standard was first published by the W3C in February 1998, rather late in this history and after the semi-structured data term had achieved some impact.[10] Dan Suciu was the first to publish on the linkage of XML to semi-structured data in 1998,[11] a reference that remains worth reading to this day.

In addition, the OEM (Object Exchange Model) has become the de facto model for semi-structured data. OEM is a graph-based, self-describing object instance model. It was originally introduced for the Tsimmis data integration project,[12] and provides the intellectual basis for object representation in a graph structure with objects either being atomic or complex.

How the attribute “metadata” is described and associated has itself been the attention of much standards work. Truly hundreds of description standards have been proposed from specific instances in medical terminology such as MESH to law to physics to engineering and to cross-discipline proposed standards such as the Dublin core. (Google these for a myriad of references.)

Challenges in Semi-structured Data

Semi-structured data, as for all other data structures, needs to be represented, transferred, stored, manipulated or analyzed, all possibly at scale and with efficiency. It is often easy to confuse data representation from data use and manipulation. XML provides an excellent starting basis for representing semi-structured data. But XML says little or nothing about these other challenges in semi-structured data use:

  • Data heterogeneity — the subject of data heterogeneity in federated systems is extremely complex, and involves such areas as unit or semantic mismatches, grouping mismatches, non-uniform overlap of sets, etc. “Glad” may be the same as”happy” but it may also be expressed in metric v. English units. This area is complex and subject to its own topic
  • Type inference — related to the above is the data type requiring resolution, for example, numeric data being written as text
  • Query language — actually, besides transfer standards, probably more attention has been given to query languages supporting semi-structured data, such as XQuery, than other topics. Remember, however, that a query language is the outgrowth of a data storage framework, not a determinant, and this distinction seems to be frequently lost in the semi-structured literature
  • Extensibility — inherent with the link to XML is the concept of extensibility with semi-structured data. However, it is important to realize that extensibility as used to date is in reference to data representation and not data processing. Further, data processing should occur without the need for database updates. Indeed, it is these later points that provide a key rationale for BrightPlanet‘s XSDM system
  • Storage — XML and other transfer formats are universally in text or Unicode, excellent for transferability but shitty for data storage. How these representations actually get stored (and searched, see next) is fundamental to scalable systems that support these standards
  • Retrieval — many have and are proposing native XML retrieval systems, and others have attempted to clone RDBMSs or search (text) engines for these purposes. Retrieval is closely linked to query language, but, more fundamentally, also needs to be speedy and scalable. As long as semi-structured retrieval mechanisms are poor-cousin add-ons to systems optimized for either structured or unstructured data, they will be poor performers
  • Distributed evaluation (scalability) — most semi-structured or XML engines work OK at the scale of small and limited numbers of files. However, once these systems attempt to scale to an enterprise level (of perhaps tens of thousands to millions of documents) or, god forbid, Internet scales of billions of documents, they choke and die. Again, data exchange does not equal efficient data processing. The latter deserves specific attention in its own right, which has been lacking to date
  • Order — consider a semi-structured data file transferred in the standard way (which is necessary) as text. Each transferred file will contain a number of fields and specifications. What is the efficient order of processing this file? Can efficiencienes be gained through a “structural” view of its semi-structure? Realize that any transition from text to binary (necessary for engine purposes, see above), also requires “smart” transformation and load (TL) approaches. There is virtually NO discussion of this problem in the semi-strucutred data literature
  • Standards — while XML and its variants provide standard transfer protocols, the use of back-end engines for efficient semi-structured data processing also requires prescribed transfer standards in order to gain those efficiencies. Because the engines are still totally lacking, this next level of prescribed formats is lacking as well.

Generally, most academic, open source, or other attention to these problems has been at the superficial level of resolving schema or definitions or units. Totally lacking in the entire thrust for a semi-structured data paradigm has been the creation of adequate processing engines for effiicient and scalable storage and retrieval of semi-structured data. [13]

You know, it is very strange. Tremendous effort goes into data representations like XML, but when it comes to positing or designing engines for manipulating that data the approach is to clone kludgy workarounds on to existing relational DBMSs or text search engines. Neither meet the test.

Thus, as the semantic Web and its association to semi-structured data looks forward, two impediments stand like gatekeepers blocking forward progress: 1) efficient processing engines and 2) scalable systems and architectures.


[1] D. Quass, A. Rajaraman, Y. Sagiv, J. Ullman and J. Widom, “Querying Semistructured Heterogeneous Information,” presented at Deductive and Object-Oriented Databases (DOOD ’95), LNCS, No. 1013, pp. 319-344, Springer, 1995.

[2] M. Tresch, N. Palmer, and A. Luniewski, “Type Classification of Semi-structured Data,” in Proceedings of the International Conference on Very Large Data Bases (VLDB), 1995.

[3] Serge Abiteboul, “Querying Semi-structured data,” in International Conference on Data Base Theory (ICDT), pp. 1 – 18, Delphi, Greece, 1997. See http://dbpubs.stanford.edu:8090/pub/1996-19.

[4] Peter Buneman, “Semistructured Data,” in ACM Symposium on Principles of Database Systems (PODS), pp. 117 – 121, Tucson, Arizona, May 1997. See http://db.cis.upenn.edu/DL/97/Tutorial-Peter/tutorial-semi-pods.ps.gz

[5] Peter Wood, School of Computer Science and Information Systems, Birkbeck College, the University of London. See http://www.dcs.bbk.ac.uk/~ptw/teaching/ssd/toc.html.

[6] David Loshin, “Simple Semi-structured Data,” Business Intelligence Network, October 17, 2005. See http://www.b-eye-network.com/view/1761.

[7] This example is actually quite complex and demonstrates the challenges facing “entity extraction” software. Extracted entities most often relate to the nouns or “things” within a document. Note also, for example, how many of the entities involve internal “co-referencing,” or the relation of subjects such as “he” to times such as “10 a.m” to specific dates. A good entity extraction engine helps resolve these so-called “within document co-references.”

[8] Peter Buneman, “Semistructured Data,” in ACM Symposium on Principles of Database Systems (PODS), pp. 117 – 121, Tucson, Arizona, May 1997. See http://db.cis.upenn.edu/DL/97/Tutorial-Peter/tutorial-semi-pods.ps.gz

[9] A common distinction is to call HTML “human readable” while XML is “machine readable” data.

[10] W3C, XML Development History. See http://www.w3.org/XML/hist2002.

[11] Dan Suciu, “Semistructured Data and XML,” in International Conference on Foundations of Data Organization (FODO), Kobe, Japan, November 1998. See PDF option from http://citeseer.ist.psu.edu/suciu98semistructured.html.

[12] Y. Papakonstantinou, H. Garcia-Molina and J. Widom, “Object Exchange Across Heterogeneous Information Sources,” in IEEE International Conference on Data Engineering, pp. 251-260, March 1995.

[13] Matteo Magnani and Danilo Montesi, “A Unified Approach to Structured, Semistructured and Unstructured Data,” Technical Report UBLCS-2004-9, Department of Computer Science, University of Bologna, 29 pp., May 29, 2004. See http://www.cs.unibo.it/pub/TR/UBLCS/2004/2004-09.pdf.

NOTE: This posting is part of an occasional series looking at a new category that I and BrightPlanet are terming the eXtensible Semi-structured Data Model (XSDM). Topics in this series cover all information related to extensible data models and engines applicable to documents, metadata, attributes, semi-structured data, or the processing, storing and indexing of XML, RDF, OWL, or SKOS data. A major white paper will be produced at the conclusion of the series. Stay tuned!