Non Obvious Relationship Awareness - O'Reilly Radar
Free and Open-source Social Network Analysis Software GraphChi can run very and application fraud NORA™ (Non-Obvious Relationship Awareness™). The analysis from days of thought and input determines which option (from an Where data is disconnected and relationships do not matter. and analysis that is looking for possible connections (hidden or obvious). The most interesting person I met this year at PC Forum was Jeff Jonas, founder of System Research and Development (SRD), the data mining.
In the world of intelligence, numerous government agencies are interested in identifying threats through the detection of non-obvious patterns of relationships and group communications buried in social media, email, texting and call detail records.
In life sciences, organizations can use graph analytics to conduct research in healthcare fraud for healthcare payers. In addition to the healthcare fraud detection program, other potential graph analytics use cases include healthcare treatment efficacy and outcome analysis, analyzing drugs and side effects, and the analysis of proteins and gene pathways.
In the area of personalized healthcare, a startup called Lumiata wants to scale personalized medicine by leveraging machine learning and graphic analytics to help doctors to focus on more urgent care needs and empower nurses to carry more of the diagnostic chores.
Graph Analytics can be used to address relationship-based problems in manufacturing, energy, gas exploration, travel, biology, conservation, computer chip design, chemistry, physics, higher education research, government, security, defense and many other fields. Advantages Offered by Graph Analytics A key advantage of graphs is the ease with which new sources of data and new relationships can be added.
Such an approach lies in stark contrast to traditional analytics, in which a great deal of time is spent organizing data, and the addition of new data sources requires time-consuming and error prone effort by analysts. The easy on-boarding of new data is particularly important when dealing with Big Data. Traditional analytics focus on finding answers to known questions.
Category Archives: Link Analysis
By contrast, many of the highest value applications, such as those identified above, are focused on discovery, where the questions to be answered are not known in advance. The ability to quickly and easily add new data sources or new relationships within the data when needed to support a new line of questioning is crucial for discovery, and graphs are uniquely well qualified to support these requirements.
- Emu: The Platform for Tackling Big Data Analysis Challenges
- How Can Graph Analytics Uncover Valuable Insights About Data?
- Non-Obvious Relationship Awareness (NORA)
Graph analytics also offer sophisticated capabilities for analyzing relationships, while traditional analytics focus on summarizing, aggregating and reporting on data. Use the right tool for the job. Some common graph analytic techniques include: To identify the most central entities in your network, a very useful capability for influencer marketing. It contained photos, maps, interview summaries and many other pieces of evidence connecting this man to the D.
Much of the initial information was secondhand and circumstantial, so Colbert was using it to provide further investigative leads for the Cold Case Team members. Here is where I make my quick disclaimer: Collecting information on U. Even though I would be supporting a private investigation, I was working as a defense contractor at the time and therefore felt it was important to follow the spirit of these restrictions by creating products based only upon what the Cold Case Team provided.
Neither myself nor my colleague independently searched for or collected any additional information for any part of this investigation. That being said, it was an exceptional opportunity to use analytical intelligence techniques to assist in this investigation.
Using Link Analysis Techniques in the Investigation In his meetings with various law enforcement officials, Colbert had grown frustrated that no one was taking the time to look through the dossier and consider the evidence.
A link chart is a graphic representation of the people, events, and significant items of interest such as a bank account or address associated with a particular subject. The initial link chart started with the main suspect and then drew graphic linkages to all his known associates their connections to third parties, and a host of other associations to events, locations, aliases and specific pieces of physical evidence.
There were hundreds of links to the main suspect, the many aliases he used over the years to include military records and associations that placed him in the vicinity of the Portland, Oregon area during the time of the hijacking.Non Obvious Relationship Awareness
The benefit of link analysis charts is that they do more than just show connections between entities. A link chart tells a comprehensive visual story and conveys a dynamic and detailed summary of information from the document supporting it.
This technique proved immensely successful, as the visual representation helped capture attention and interest from outside parties. How Intelligence Analysis Aided in the Investigation Besides taking text-based information and turning it into a graphic visualization for presentation purposes, a link chart also helped the investigation in other ways. Where NORA could help improve national security by discovering threats of a terroristic nature.
Later in it was acquired by IBM.
How Can Graph Analytics Uncover Valuable Insights About Data?
In order for technologies such as NORA to work effectively, a certain degree of data integrity is imperative. Name standardisation — transposing nicknames to their root name such as Rich, Richie, and Rick to Richard ; Address hygiene — correcting typos and abbreviations Rd.
Furthermore, relationships are also recorded and explored. Where standard queries run at intervals, NORA is continuously scanning and assessing new information. This is one of its greatest strengths as terrorist attacks are time-sensitive events. While there is a limit to how much information a human could process, such limits are much greater, if not non-existent given enough resources, for machines. Furthermore, identifying non-obvious relationships in small datasets is difficult for humans.
Increase the datasets and even in a perfect world a human would be unable to do this with perfectly matched databases.