Using “smart information products” to track the effect and value of information products through the life of the information.
Status: The funding round closed on 30 November 2016. Grant applications are no longer being accepted.
Both AUSTRAC and Australian Criminal Intelligence Commission (ACIC) are facing an increase in the scope and volume of information collected, analysed and shared in numerous sectors, both nationally and internationally. This volume and diversity poses both a policy and a service delivery problem, which can be expressed as “how can producers of information determine whether their products are effective, adding value and the best allocation of resources to produce the information in the first place?”
Without this knowledge policy makers and operational areas do not have a great array of indicators for improving their products and making them relevant, as feedback typically has been burdensome and not a priority for the user. This impacts the cost and future designs of information systems to better produce, manage and exchange the right information.
Options to overcome this could utilise technology such as Block-Chain to create “smart information products” that would allow the tracking of the outcome of the information and intelligence the agency provides, history of its use, and user feedback through the life of the information.
What does the challenge seek?
Specifically, the challenge seeks to:
Allow users to push or pull targeted and relevant information
- Information products can be disseminated and published independently of a system.
- Consumers can better target relevant information products with minimal search parameters.
- Access to information products (in whole or in part) can be controlled.
- Historical data and content within an information product can persist.
- Intelligent mark up or metadata within an information product allows users to find what is relevant quickly.
- Information is structured in a useful pattern without assumptions of who may need to see the product.
Allow persistent feedback irrespective of where the product is located
- Feedback can be collected both specifically and generally allowing creators of information to improve their product.
- Feedback can be accessed or received irrespective of where the information product is. This would allow feedback to be associated to the product, not the producer and give insights to the information’s usage, relevancy, associated business outcomes, longevity, suggestions and audit.
- Motivates users of information to give feedback promptly closest to point of usage and not an afterthought. It should be easy, promote consistency and quality, and facilitate positive relationships between producer and consumer.
- Feedback is persistent for the life of the information product.
- Aggregate feedback irrespective of how many copies or instances are being used or whether there are disparate components of information having received feedback.
Ensure use of information can be traceable and visible
- Producers of information can understand who is reading the information and how they are using it.
- Consumers of information can validate the reliability of the information source.
- An audit history of the information can show any further sharing of information to other consumers.
- History of changes and associations to other information products are retained.
- Ability to highlight a link between an information product and successful outcomes, such as a prosecution, investigations or other business outcome.
Ensure information is still secure and meets legislative requirements
- A smart information product should still meet legislative requirements on privacy and protecting intellectual property.
- Information products such as intelligence products and feedback must not get into the wrong hands; secure information product sharing must meet government security classification standards.
- Information sharing and management has to consider legislative requirements around record keeping.
- Self-destruct component that will automatically destroy all or part information under certain conditions.
- Share components of information based on user clearance and security.
Facilitate better relationships between Government and Industry roles
- Information provided across federal and state governments, industry and internationally should promote better relationships by assisting producers to improve the relevancy and usefulness of their information. It will help producers understand more clearly the role of a consumer and how the information is intended and could be used.
- Delineating across the network of providers of higher quality information products.
- Support de-confliction by knowing who else is looking at and using specific information products. This can improve collaboration on joint taskforces and initiatives.
- Enables producers to gain insights as to who else may be interested in their information products.
- Can elicit a whole of industry and government view as information distributed across a broader network and shared widely can aggregate feedback of the information product as it is shared and used multiple times over.
What are the constraints?
Constraints to be considered include:
- Training of producers of information to minimise any burden or overhead.
- Motivating producers and consumers of information in shifting their behaviour to provide feedback, including elapsed time between the initial consumption of the information and the ability to provide feedback on its impact.
- Working within the current information security requirements of users and any exchange of information between departments, agencies and private sector businesses.
- Working within the current information management requirements, including application of minimum metadata standards and accountable disposal requirements.
What are the deliverables?
Deliverables at the end of the proof of concept phase would include a working prototype utilising technology comprising of:
- User interfaces to read information additional to the protected PDF or xml mark-up type document
- Platform - device agnostic
- Use of technology (like Block-Chain) for housing and transporting information securely
- Add additional information to primary smart information “document / product” at any time and not lose history (feedback, user history, usage, outcome and relevance score) at point of usage and have it persistent and able to retrieve at any time by authorised users.
- Send and receive information to users within and outside the network
- Open additional information (feedback, history, outcome, links between products and relevance score)
- Minimum two participants
- Ability to append and track minimum of three history events
- Interoperable between agencies systems and physical locations
These will be trialled by:
- Field simulation and test
- Operate within ACIC and AUSTRAC networks
On site requirements
Depending on the nature of the feasibility study or prototype, access may entail use of ‘dirty systems’ gained through physical site or specified web server. Full solution ought to be inspected by Australian Signals Directorate and installed behind client’s own firewall.
Where Blockchain type approaches are used there are essentially two technical problems:
The first relates to the (semi)-automatic integration of disparate log data in an immutable ledger, like that provided by the Blockchain technology.
There are now good Blockchain 2.0 platforms like Ethereum on which the Challenge application could be built. Semi-automatic data integration can be tackled in two ways, via Ontologies or Machine Learning techniques for detecting data matches. Here is a quick introduction to Ontologies.
Here is a representative machine learning approach to the data integration problem.
The second technical problem in the challenge relates to the credit assignment problem in the general area of sequential decision making under uncertainty.
In a complex investigation, the ultimate success (or failure) is associated with a vast number of events/information products/decisions.
When we do get feedback on a successful investigation, how do we assign credit among the multitude of information products and decisions that led to the success?
Problems like these are studied in the area of Markov Decision Processes and the closely related Dynamic Programming solution technique in the literature.
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