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Analytics for Cyber Conflict -Part Deux

Part 1 in this series is avaiable at http://www.decisionstats.com/analytics-for-cyber-conflict/

The next articles in this series will cover-

  1. the kind of algorithms that are currently or being proposed for cyber conflict, as well as or detection

Cyber Conflict requires some basic elements of the following broad disciplines within Computer and Information Science (besides the obvious disciplines of heterogeneous database types for different kinds of data) -

1) Cryptography – particularly a cryptographic  hash function that maximizes cost and time of the enemy trying to break it.

From http://en.wikipedia.org/wiki/Cryptographic_hash_function

The ideal cryptographic hash function has four main or significant properties:

  • it is easy (but not necessarily quick) to compute the hash value for any given message
  • it is infeasible to generate a message that has a given hash
  • it is infeasible to modify a message without changing the hash
  • it is infeasible to find two different messages with the same hash

A commercial spin off is to use this to anonymized all customer data stored in any database, such that no database (or data table) that is breached contains personally identifiable information. For example anonymizing the IP Addresses and DNS records with a mashup  (embedded by default within all browsers) of Tor and MafiaaFire extensions can help create better information privacy on the internet.

This can also help in creating better encryption between Instant Messengers in Communication

2) Data Disaster Planning for Data Storage (but also simulations for breaches)- including using cloud computing, time sharing, or RAID for backing up data. Planning and creating an annual (?) exercise for a simulated cyber breach of confidential just like a cyber audit- similar to an annual accounting audit

3) Basic Data Reduction Algorithms for visualizing large amounts of information. This can include

  1. K Means Clustering, http://www.jstor.org/pss/2346830 , http://www.cs.ust.hk/~qyang/Teaching/537/Papers/huang98extensions.pdf , and http://stackoverflow.com/questions/6372397/k-means-with-really-large-matrix
  2. Topic Models (LDA) http://www.decisionstats.com/topic-models/,
  3. Social Network Analysis http://en.wikipedia.org/wiki/Social_network_analysis,
  4. Graph Analysis http://micans.org/mcl/ and http://www.ncbi.nlm.nih.gov/pubmed/19407357
  5. MapReduce and Parallelization algorithms for computational boosting http://www.slideshare.net/marin_dimitrov/large-scale-data-analysis-with-mapreduce-part-i

In the next article we will examine

  1. the role of non state agents as well as state agents competing and cooperating,
  2. and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Oracle launches XBRL extension for financial domains

What is XBRL and how does it work?

http://www.xbrl.org/HowXBRLWorks/

How XBRL Works
XBRL is a member of the family of languages based on XML, or Extensible Markup Language, which is a standard for the electronic exchange of data between businesses and on the internet.  Under XML, identifying tags are applied to items of data so that they can be processed efficiently by computer software.

XBRL is a powerful and flexible version of XML which has been defined specifically to meet the requirements of business and financial information.  It enables unique identifying tags to be applied to items of financial data, such as ‘net profit’.  However, these are more than simple identifiers.  They provide a range of information about the item, such as whether it is a monetary item, percentage or fraction.  XBRL allows labels in any language to be applied to items, as well as accounting references or other subsidiary information.

XBRL can show how items are related to one another.  It can thus represent how they are calculated.  It can also identify whether they fall into particular groupings for organisational or presentational purposes.  Most importantly, XBRL is easily extensible, so companies and other organisations can adapt it to meet a variety of special requirements.

The rich and powerful structure of XBRL allows very efficient handling of business data by computer software.  It supports all the standard tasks involved in compiling, storing and using business data.  Such information can be converted into XBRL by suitable mapping processes or generated in XBRL by software.  It can then be searched, selected, exchanged or analysed by computer, or published for ordinary viewing.

also see

http://www.xbrl.org/Example1/

 

 

 

and from-

http://www.oracle.com/us/dm/xbrlextension-354972.html?msgid=3-3856862107

With more than 7,000 new U.S. companies facing extensible business reporting language (XBRL) filing mandates in 2011, Oracle has released a free XBRL extension on top of the latest release of Oracle Database.

Oracle’s XBRL extension leverages Oracle Database 11g Release 2 XML to manage the collection, validation, storage, and analysis of XBRL data. It enables organizations to create one or more back-end XBRL repositories based on Oracle Database, providing secure XBRL storage and query-ability with a set of XBRL-specific services.

In addition, the extension integrates easily with Oracle Business Intelligence Suite Enterprise Edition to provide analytics, plus interactive development environments (IDEs) and design tools for creating and editing XBRL taxonomies.

The Other Side of XBRL
“While the XBRL mandate continues to grow, the feedback we keep hearing from the ‘other side’ of XRBL—regulators, academics, financial analysts, and investors—is that they lack sufficient tools and historic data to leverage the full potential of XBRL,” says John O’Rourke, vice president of product marketing, Oracle.

However, O’Rourke says this is quickly changing as XBRL mandates enter their third year—and more and more companies have to comply. While the new extension should be attractive to organizations that produce XBRL filings, O’Rourke expects it will prove particularly valuable to regulators, stock exchanges, universities, and other organizations that need to collect, analyze, and disseminate XBRL-based filings.

Outsourcing, a Bolt-on Solution, or Integrated XBRL Tagging
Until recently, reporting organizations had to choose between expensive third-party outsourcing or manual, in-house tagging with bolt-on solutions— both of which introduce the possibility of error.

In response, Oracle launched Oracle Hyperion Disclosure Management, which provides an XBRL tagging solution that is integrated with the financial close and reporting process for fast and reliable XBRL report submission—without relying on third-party providers. The solution enables organizations to

  • Author regulatory filings in Microsoft Office and “hot link” them directly to financial reporting systems so they can be easily updated
  • Graphically perform XBRL tagging at several levels—within Microsoft Office, within EPM system reports, or in the data source metadata
  • Modify or extend XBRL taxonomies before the mapping process, as well as set up multiple taxonomies
  • Create and validate final XBRL instance documents before submission

 

Towards better quantitative marketing

Cycle of Research and Development, from "...

Image via Wikipedia

The term quantitative refers to a type of information based in quantities or else quantifiable data (objective properties) —as opposed to qualitative information which deals with apparent qualities (subjective properties)

http://en.wikipedia.org/wiki/Quantitative

Fear, uncertainty, and doubt (FUD) is a tactic of rhetoric and fallacy used in sales, marketing, public relations,[1][2] politics and propaganda. FUD is generally a strategic attempt to influence public perception by disseminating negative and dubious/false information designed to undermine the credibility of their beliefs.

Source-

http://en.wikipedia.org/wiki/Fear,_uncertainty_and_doubt

Top 5 FUD Tactics in Software and what you can say to end user to retain credibility

1) That software lacks reliable support- our support team has won top prizes in Customer Appreciation for past several years.

  • Our software release history-
  • graph of bugs filed-
  • turn around time box plot for customer service issues
  • quantitatively define reliability

2) We give the best value to customers. Customer Big A got huge huge % savings thanks to our software.

  • Pricing- Transparent – and fixed. For volume discounts mention slabs.
  • Cost to Customer- Include time and cost estimates for training and installation
  • Graphs of average ROIC (return on capital invested) on TCO (total cost of ownership)  not half a dozen outlier case studies. Mention Expected % return

3) We have invested a lot of money in our Research and Development. We continue to spend a lotto of money on R &D

  • Average Salary of R and D employee versus Average Tenure (Linkedin gives the second metric quite easily)
  • Mention Tax benefits and Accounting treatment of R&D expenses
  • Give a breakdown- how much went to research and how much went to legacy application support
  • Mention open source projects openly
  • Mention community source projects separately

4) Software B got sued. Intellectual property rights (sniff)

  • Mention pending cases with your legal team
  • Mention anti trust concerns for potential acquisitions
  • Mention links to your patent portfolio (or even to US PTO with query ?=your corporate name )

5) We have a 99.8% renewal rate.

  • Mention vendor lock in concerns and flexibility
  • Mention What-If scenarios if there are delays in software implementation
  • Mention methodology in calculating return on investment.

 

 

 

Also

http://blogs.computerworlduk.com/infrastructure-and-operations/2010/10/three-fud-statements-used-not-to-implement-standards-based-networking/index.htm

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