 users' tags. Pivoting on known user information is a big advantage of tagging inside the enteprise (as compared to anonymous social tagging such as available on delicious).
One of the truly powerful aspects of integrating tagging into search (tags are applied at the time a search result is displayed) is that it integrates tagging into normal work flow. Users search all the time; they don't have to go a separate system to enter a tag, and the tags are immediately displayed and available for their own use the next time around.
I am a huge fan of social tagging. It is a key aspect of Enterprise 2.0, will greatly enhance findability for individual users, and then will only get better as more and more people add their own targeted markers of significance and relevancy. Ranking and commenting also have great potential for enhancing search inside the enterprise.
Guided Navigation
Like other enterprise-class search tools, IUS displays sets of results clustered by key metadata alongside a directly accessible relevancy-ranked set of search results. Where a collection contains significant metadata, drilling down into a set of search results through clusters of metadata is much more effective than a simple ranked list, because it lets users identify resources with directly relevant attributes or whose attributes are close enough to be useful. It also might let a searcher exclude large sets of returned documents.
For instance, a litigator looking for a Markman brief in patent litigation where guided navigation is available might search across all courts, but then drill down into the particular jurisdiction or into jurisdictions in the same federal circuit.
Semantic or Concept Clustering
In addition to clustering into categories predefined by DMS administrators, the Vivisimo engine has the remarkable ability to group documents into clusters based on concepts extracted from the documents. Vivisimo's "calling card" is its ability to generate clusters of documents based on similarities between groups of documents; at the same time, it identifies the groups by what it determines to be key characteristics of that "cluster." Depending on what the settings are, there can be clusters within clusters, allowing you to drill down into the set of documents most relevant to your search (instead of starting the search over when you get too many hits).
See for instance this clusty.com search for "senators", which lists "members" (of Congress) "Ottowa Senators" (the hockey team), "Legislation, Law" (which includes state senate information), and so forth.
Expertise Identification
The out-of-the-box people search looks at document authorship and official firm website biographies. Expertise can also be identified through looking at who is tagging what, which is exposed both through the lens of individual tags and through a view of a user profile. I suspect it would be also possible to use IUS/LSA to incorporate and analyze billing and matter records to further refine the people search.
Searching Across Silos
A key challenge for KM and information management strategy is the distribution of enterprise content across mutually exclusive “silos” of information. A matters database might have information about the general type of work being done on a particular deal and the partner who opened the matter; a document management system might have documents tagged with those same matter numbers; a separate system might contain final pleadings or vetted content; and a billing system might indicate who has worked on that matter the most. Typically the three sets of data never meet, and certainly cannot be leveraged in any reasonably user-friendly manner.
Enterprise search, of whatever platform, provides the “glue” that can bring all of these pieces together. A search for Delaware patent litigation briefs through such an engine might identify not just the Markman brief that the attorney was looking for, but also patent matters and people who with previous experience from that jurisdiction. In some use cases an enterprise search might even extent to designated or licensed content from the internet or proprietary databases.
IUS/LSA clusters based on source as well as documents' content and metadata.
Spotlights
IUS also has "Spotlights", very much like “Best Bets” or canned prepared sets of responses to particular queries. Knowledge management or other firm groups can promote particular sets of precedents or practice guide. In the demo, a search for "motion philadelphia" gets "How to file a motion in Philadelphia Court of Common Pleas."
This kind of spotlighting ties in well with efforts to provide procedural guidance for litigators, or deal checklist assistance to junior business lawyers. I am engaged on a wiki-based project to gather just this kind of information (on the litigation side).
Metadata
The search previewed here will also add metadata about court and judge to that about cases and statutory references. This is through a trick known as “entity extraction”, where key parties or case numbers from the documents are matched up with party, case, and judge information from Lexis’ massive databases. I am not sure if this feature has been publicly released.
Profiling
They also demonstrated automatic document profiling, through which documents can get assigned document types such as memo, pleading, correspondence, and so forth, based on their content and similarity to comparison sets of documents. I do not believe this feature has been publicly released either.
Information Gravitation
Some clients are using search to populate client pages that combine information from internal sources and saved searches of the web, in Sharepoint web parts. They have a federated search connector for Google, or, can search particular sites.
Such pages highlight enterprise search's ability to expose and make useful content previously buried in unconnected databases.According to the latest data available from the Statistics Institute of Turkey, there are currently about 2,500 journals and newspapers published regularly. Yet studies on periodicals are limited with publications of journal catalogs and guides. Studies based on the analytical investigation of articles appeared in those journals are scarce. Findings of such studies are important because they will not only help identify longitudinal trends with regards to scientific publishing in a country, but also will reveal the bibliometric features of articles that appear in scientific and professional journals in various subjects. This project aimed to conduct a detailed bibliometric investigation of articles along with their subjects and authors that were published in periodicals that appeared between 1923 and 1999 with a view to contribute to the mapping of science and creating a science and technology “road map” in Turkey. Data were gathered through the Bibliography of Articles of the Republican Era1923-1999 (BARE)that was recently published on CD-ROM by the Turkish National Library. Records belonging to a total of 566,627 articles were transferred to a statistical software package (SPSS) to study the bibliometric characteristics thereof. The scatter of articles to journals and the author productivity were tested to see if they fit Bradford and Lotka laws. Words that were used most frequently in article titles were identified. Human-computer interaction (HCI) issues and the usability criteria seem to have not been taken into account when designing the user interface of the BARE CD-ROM. Designers did not seem to carry out a comprehensive systems analysis study before designing the database and they did not take into account the principles of database management design. Nine percent of the records contained errors in article and journal titles as well as authors’ names. Two-thirds of authors articles indexed in BARE are of technology and social sciences. Biomedical articles constitute of one fifth of all articles. An overwhelming majority of articles were authored by a single author. Scatter of articles did not fit the Bradford Law, the 80/20 rule, and the Price Law. Yet, a relatively few “core” journal titles in each subject contained a significant percentage of all articles published in that subject. The most productive authors in each subject were also identified. Author productivity data in some disciplines (technology, fine arts, literature and rhetorics, and geography and history) did not fit the Lotka Law. Lotka’s αvalues ranged between 2.03 and 2.55 for various subjects. This study provides long-term bibliometric data on articles published in Turkish journals. Bibliometric findings obtained will, we hope, pave the way for further and more comprehensive studies on related subjects, contribute to the creation of the science and technology road map of Turkey and the development of collection management policies in libraries. Project report ends with some recommendations based on findings.This image shows gullies in an unnamed crater approximately 25 kilometers (16 miles) in diameter and 1.2 kilometers (0.75 miles) deep, located south of Terra Sirenum. Several features associated with this crater suggest that, at some point in time, liquid and ice may have been present at and near the surface.
A MOC context image of this crater shows the typical lobate ejecta pattern characteristic of rampart craters, interpreted by some as indicating ice-rich or water-rich materials was excavated at the time of impact. According to this theory, the rock debris ejected from the crater and mixed with the water flowed more easily along the surface. Other possible interpretation is that fine-grained rocky material mixed with air and formed the rampart pattern in the absence of water.
The HiRISE image shows gullies not only in the inside of the crater, but also in the outside, emerging from the elevated rim of the crater (NW corner of the image). The subimage shows one of the outside gullies; illumination is from the left, downhill is up. The box-like shape of its alcove suggest this gully may have been produced by groundwater sapping, as opposed to surface runoff.
Polygon patterns are ubiquitous in this image: on the walls of the crater, on its floor, and on the walls of some of the gullies. These polygons are similar to features observed in terrestrial permafrost such as in the Arctic and Antarctic. Polygonal patterns are produced by repeated expansion and contraction of frozen soil, due to seasonal temperature cycles.Bring fresh florals to your