El Proceso de cristo - Ignacio Burgoa Orihuela. (Libro)Cybernautic.
|Perspectiva Jurídica del Proceso Judicial que paso Jesucristo aquí en la tierra|
____________________________________________________
"Jesús respondió y les dijo: Destruid este templo, y en tres días lo levantaré" - Juan 2:19.
"Y enseñaba diciendo: El Hijo del Hombre debe padecer mucho, y ser rechazado por los ancianos, los principales sacerdotes y los escribas, y ser muerto, y resucitar al tercer día" - Lucas 9:22.
"Y Jesús decía: Padre, perdónalos, porque no saben lo que hacen" - Lucas 23:34.
"Mas ve tú, y aprende el significado de: MISERICORDIA QUIERO Y NO SACRIFICIO; porque no he venido a llamar a justos, sino a pecadores"- Mateo 9:13.
El Proceso de cristo - Ignacio Burgoa Orihuela. (Libro)Cybernautic.
|Perspectiva Jurídica del Proceso Judicial que paso Jesucristo aquí en la tierra|
____________________________________________________
"Jesús respondió y les dijo: Destruid este templo, y en tres días lo levantaré" - Juan 2:19.
"Y enseñaba diciendo: El Hijo del Hombre debe padecer mucho, y ser rechazado por los ancianos, los principales sacerdotes y los escribas, y ser muerto, y resucitar al tercer día" - Lucas 9:22.
"Y Jesús decía: Padre, perdónalos, porque no saben lo que hacen" - Lucas 23:34.
"Mas ve tú, y aprende el significado de: MISERICORDIA QUIERO Y NO SACRIFICIO; porque no he venido a llamar a justos, sino a pecadores"- Mateo 9:13.
How Taxonomies and facets bring end users closer to big dataPeter Wren-Hilton
Pingar researcher Dr Anna Divoli's presentation given at the 2012 Text Analytics World Boston. Content includes discussion of taxonomies and big data,.
Presentation that won the SharePoint Idol competition at the 2011 New Zealand SharePoint Conference. Demonstrates how the Pingar technology can automatically populate metadata fields in SharePoint document collections.
Pingar chief research officer Alyona Medelyan presents research conducted jointly with Anna Divoli at the Human Computer Information Retrieval workshop 2011.
Presented at Semantic Garage Meetup San Francisco 2011. Unstructured data comes at a high cost - $37,000 per year per person in information industries. By using tools to automatically add metadata enterprises can improve search results, speed e-discovery and risk assessment, summarize content and extract entities from files. Unstructured and semi-structured data represents a large component of big data. By turning unstructured content into business intelligence, enterprise can speed time to information.
Mining Unstructured Data:Practical Applications, from the Strata O'Reilly Mak...Peter Wren-Hilton
Alyona Medelyan (Pingar), Anna Divoli (Pingar)
presented at Strata O'Reilly Making Data Work Conference on March 1, 2012
The challenge of unstructured data is a top priority for organizations that are looking for ways to search, sort, analyze and extract knowledge from masses of documents they store and create daily. Text mining uses knowledge-driven algorithms to make sense of documents in a similar way a person would do by reading them. Lately, text mining and analytics tools became available via APIs, meaning that organizations can take immediate advantage these tools. We discuss three examples of how such APIs were utilized to solve key business challenges.
Most organizations dream of paperless office, but still generate and receive millions of print documents. Digitizing these documents and intelligently sharing them is a universal enterprise challenge. Major scanning providers offer solutions that analyze scanned and OCR’d documents and then store detected information in document management systems. This works well with pre-defined forms, but human interaction is required when scanning unstructured text. We describe a prototype build for the legal vertical that scans stacks of paper documents and on the fly categorizes and generates meaningful metadata.
In the area of forensics, intelligence and security, manual monitoring of masses of unstructured data is not feasible. The ability of automatically identify people’s names, addresses, credit card and bank account numbers and other entities is the key. We will briefly describe a case study of how a major international financial institution is taking advantage of text mining APIs in order to comply with a recent legislation act.
In healthcare, although Electronic Health Records (EHRs) have been increasingly becoming available over the past two decades, patient confidentiality and privacy concerns have been acting as obstacles from utilizing the incredibly valuable information they contain to further medical research. Several approaches have been reported in assigning unique encrypted identifiers to patients’ ID but each comes with drawbacks. For a number of medical studies consistent uniform ID mapping is not necessary and automated text sanitization can serve as a solution. We will demonstrate how sanitization has practical use in a medical study.
And read a full interview with Alyona and Anna at http://radar.oreilly.com/2012/02/unstructured-data-analysis-tools.html
How Taxonomies and facets bring end users closer to big dataPeter Wren-Hilton
Pingar researcher Dr Anna Divoli's presentation given at the 2012 Text Analytics World Boston. Content includes discussion of taxonomies and big data,.
Presentation that won the SharePoint Idol competition at the 2011 New Zealand SharePoint Conference. Demonstrates how the Pingar technology can automatically populate metadata fields in SharePoint document collections.
Pingar chief research officer Alyona Medelyan presents research conducted jointly with Anna Divoli at the Human Computer Information Retrieval workshop 2011.
Presented at Semantic Garage Meetup San Francisco 2011. Unstructured data comes at a high cost - $37,000 per year per person in information industries. By using tools to automatically add metadata enterprises can improve search results, speed e-discovery and risk assessment, summarize content and extract entities from files. Unstructured and semi-structured data represents a large component of big data. By turning unstructured content into business intelligence, enterprise can speed time to information.
Mining Unstructured Data:Practical Applications, from the Strata O'Reilly Mak...Peter Wren-Hilton
Alyona Medelyan (Pingar), Anna Divoli (Pingar)
presented at Strata O'Reilly Making Data Work Conference on March 1, 2012
The challenge of unstructured data is a top priority for organizations that are looking for ways to search, sort, analyze and extract knowledge from masses of documents they store and create daily. Text mining uses knowledge-driven algorithms to make sense of documents in a similar way a person would do by reading them. Lately, text mining and analytics tools became available via APIs, meaning that organizations can take immediate advantage these tools. We discuss three examples of how such APIs were utilized to solve key business challenges.
Most organizations dream of paperless office, but still generate and receive millions of print documents. Digitizing these documents and intelligently sharing them is a universal enterprise challenge. Major scanning providers offer solutions that analyze scanned and OCR’d documents and then store detected information in document management systems. This works well with pre-defined forms, but human interaction is required when scanning unstructured text. We describe a prototype build for the legal vertical that scans stacks of paper documents and on the fly categorizes and generates meaningful metadata.
In the area of forensics, intelligence and security, manual monitoring of masses of unstructured data is not feasible. The ability of automatically identify people’s names, addresses, credit card and bank account numbers and other entities is the key. We will briefly describe a case study of how a major international financial institution is taking advantage of text mining APIs in order to comply with a recent legislation act.
In healthcare, although Electronic Health Records (EHRs) have been increasingly becoming available over the past two decades, patient confidentiality and privacy concerns have been acting as obstacles from utilizing the incredibly valuable information they contain to further medical research. Several approaches have been reported in assigning unique encrypted identifiers to patients’ ID but each comes with drawbacks. For a number of medical studies consistent uniform ID mapping is not necessary and automated text sanitization can serve as a solution. We will demonstrate how sanitization has practical use in a medical study.
And read a full interview with Alyona and Anna at http://radar.oreilly.com/2012/02/unstructured-data-analysis-tools.html