Actionable Intelligence Research Articles

Creating a Community of Practice to Support Data Linkage Across Government Agencies

IntroductionSince 2008, our team has studied sites with strong track records of integrating cross-sector data to address complex social problems. We have identified best practices and are supporting 16 sites with individualized technical assistance and participation in a learning community to facilitate data sharing that leads to actionable intelligence.
 Objectives and ApproachThis presentation will provide an overview of our findings in studying established integrated data systems from across the United States, and a summary of our efforts in supporting the development of new integrated data systems (IDS). Specifically, we will answer:
 
 What are the core features of integrated data systems (IDS) needed to support data linkage across government agencies?
 How can sites develop these core features to facilitate data sharing, and ultimately, actionable intelligence?
 What are the common roadblocks to successfully sharing data across agency siloes?
 How can these roadblocks be addressed to create a sustainable data sharing ecosystem?
 
 ResultsAgencies collect vast amounts of data, yet struggle to assess program impacts due to data siloes. The ability to link data across agencies to develop potential policy and service improvements is difficult, and at times, impossible. This technical assistance supports a different approach.
 Our Integrated Data System (IDS) Learning Community initiative is an 18-month engagement for sites selected through a competitive process. As of September 2018, sites will be 11 months into the IDS development process. This presentation will discuss site-based approaches to facilitate data sharing, with an emphasis on governance, foundational agreements, data management and security, and stakeholder driven processes that ensure ethical use of data. This discussion will include progress to date, common challenges and solutions to developing an IDS.
 Conclusion/ImplicationsWhile there is broad agreement in the value of integrating data across domains, developing the capacity and skills necessary to link administrative data for policy evaluation and research remains an elusive goal. Initial results indicate that an individualized yet collaborative technical assistance approach is successful in developing data integration capacity.

The International Population Data Linkage Network – Banff and Beyond

We write to you, here in the pages of the International Journal of Population Data Science, for the second time in our capacity of co-directors of the International Population Data Linkage Network (IPDLN – www.ipdln.org). Time has certainly passed quickly since our first communication, where we introduced ourselves, and discussed planned initiatives for our tenure as leads of the IPDLN. Our network’s scientific community is steadily growing and thriving in an era of heightened interest around all things ‘data’. Indeed, there is great enthusiasm for all initiatives that explore ways of harnessing information systems and multisource data to enhance collective knowledge of health matters so that better decisions can be made by governments, system planners, providers, and patients. Never before have such initiatives attracted more attention.
 It is in this context of heightened interest and relevance around IPDLN and its science that we prepare to convene in Banff, Alberta, Canada for the 5th biennial IPDLN Conference – September 11-14. The conference, to be held at the inspiring Banff Centre (www.banffcentre.ca), is almost sold out, with only limited space remaining for late registrants. A tremendous program has been created through the oversight of Scientific Program co-chairs, Drs. Astrid Guttman and Hude Quan. A compelling roster of plenary lectures from Drs. Diane Watson, Jennifer Walker, and Osmar Zaïane is eagerly anticipated, as are topical panel discussions, an entertaining Science Slam session, and a terrific social program. These sessions will be surrounded by rich scientific oral and poster presentations arising from the more than 450 scientific abstracts submitted for review. We are so pleased to see this vibrant scientific engagement from the IPDLN membership and students, and look forward to hosting all delegates in Banff.
 The Banff conference will also be the venue at which we announce the new Directorship of the IPDLN for the next two years (2019 and 2020). As co-directors, we engaged with a number of individuals and organizations with interest in leading the IPDLN. In the end, two compelling Directorship applications were submitted – one a joint bid from Australia’s Population Health Research Network and the South Australia Northern Territory DataLink, and the other from the US-based Actionable Intelligence for Social Policy. IPDLN members submitted votes on these strong leadership bids through an online voting process, and while the excellence and appeal of both bids was apparent in strong voter support for both, a winning bid has been confirmed, and it will (as mentioned) be announced at the upcoming September conference.
 As we look forward to the Banff meeting with great anticipation, we are compelled to acknowledge the growing IPDLN legacy created by past directors. We are particularly indebted to our immediate predecessor, Dr. David Ford, and his team at Swansea University. Their work in hosting the 2016 IPDLN conference has been an inspiration to us in the planning of this year’s conference, and their crucial and foundational work in creating an IT platform for the IPDLN website, the membership database, and the new International Journal for Population Data Science has brought the IPDLN to a new level of organizational sophistication. Over the last 18 months, our co-directorship teams from the Institute for Clinical Evaluative Sciences in Ontario and the O’Brien Institute for Public Health at the University of Calgary have built on the foundation established by prior directors to update/enhance the IPDLN website and membership database. The IPDLN has more members than ever before representing a greater number of countries, and we have a more formalized governance structure with the creation of an Executive Committee that will include immediate past-Directors in order to better ensure continuity. A new Executive Committee will be elected by the IPDLN membership following the Banff conference.
 The waiting is almost over and IPDLN 2018 is upon us! Our scientific domain has never had the prominence or level of anticipation that we currently see. And the IPDLN has grown in its size, vibrancy and scientific scope. The opportunities for us are boundless, and the timing of our upcoming conference could not be better. We are honoured, with our respective organizations, to have had this opportunity to serve as co-directors over the past two years, and look forward to seeing many of you very soon. For those of you who are unable to travel to Canada’s Rocky Mountains this year, we look forward to connecting with you at a later time in the IPDLN’s continuing upward journey.

Geography Information System (GIS) and Geography Teaching Material

GIS technology applies geographic science with tools for understanding and collaboration. It helps people reach a common goal: to gain actionable intelligence from all types of data. GIS integrates many different kinds of data layers using spatial location. Most data has a geographic component. GIS data includes imagery, features, and basemaps linked to spreadsheets and tables. Spatial analysis lets you evaluate suitability and capability, estimate and predict, interpret and understand, and much more, lending new perspectives to your insight and decision-making.

A Novel Trust Taxonomy for Shared Cyber Threat Intelligence

Cyber threat intelligence sharing has become a focal point for many organizations to improve resilience against cyberattacks. The objective lies in sharing relevant information achieved through automating as many processes as possible without losing control or compromising security. The intelligence may be crowdsourced from decentralized stakeholders to collect and enrich existing information. Trust is an attribute of actionable cyber threat intelligence that has to be established between stakeholders. Sharing information about vulnerabilities requires a high level of trust because of the sensitive information. Some threat intelligence platforms/providers support trust establishment through internal vetting processes; others rely on stakeholders to manually build up trust. The latter may reduce the amount of intelligence sources. This work presents a novel trust taxonomy to establish a trusted threat sharing environment. 30 popular threat intelligence platforms/providers were analyzed and compared regarding trust functionalities. Trust taxonomies were analyzed and compared. Illustrative case studies were developed and analyzed applying our trust taxonomy.

The Mental Representation of Human Action.

Various theories of moral cognition posit that moral intuitions can be understood as the output of a computational process performed over structured mental representations of human action. We propose that action plan diagrams-"act trees"-can be a useful tool for theorists to succinctly and clearly present their hypotheses about the information contained in these representations. We then develop a methodology for using a series of linguistic probes to test the theories embodied in the act trees. In Study 1, we validate the method by testing a specific hypothesis (diagrammed by act trees) about how subjects are representing two classic moral dilemmas and finding that the data support the hypothesis. In Studies 2-4, we explore possible explanations for discrete and surprising findings that our hypothesis did not predict. In Study 5, we apply the method to a less well-studied case and show how new experiments generated by our method can be used to settle debates about how actions are mentally represented. In Study 6, we argue that our method captures the mental representation of human action better than an alternative approach. A brief conclusion suggests that act trees can be profitably used in various fields interested in complex representations of human action, including law, philosophy, psychology, linguistics, neuroscience, computer science, robotics, and artificial intelligence.

DomainChroma: Building actionable threat intelligence from malicious domain names

Since the 1980s, domain names and the domain name system (DNS) have been used and abused. Although legitimate Internet users rely on domain names as indispensable infrastructures for using the Internet, attackers use or abuse them as reliable, instantaneous, and distributed attack infrastructures. However, there is a lack of complete understanding of such domain-name abuses and methods for coping with them.In this study, we designed and implemented a unified analysis system combining current defense solutions to build actionable threat intelligence from malicious domain names. The basic concept underlying our system is malicious domain name chromatography. Our analysis system can distinguish among mixtures of malicious domain names for websites. On the basis of this concept, we do not create a hodgepodge of current solutions but design separation of abused domain names and offer actionable threat intelligence or defense information by considering the characteristics of malicious domain names as well as the possible defense solutions and points of defense. Finally, we evaluated our analysis system and defense-information output using a large real dataset to show the effectiveness and validity of our system.

Cyber Threat Intelligence – Issue and Challenges

Today threat landscape evolving at the rapid rate with many organization continuously face complex and malicious cyber threats. Cybercriminal equipped by better skill, organized and well-funded than before. Cyber Threat Intelligence (CTI) has become a hot topic and being under consideration for many organization to counter the rise of cyber-attacks. The aim of this paper is to review the existing research related to CTI. Through the literature review process, the most basic question of what CTI is examines by comparing existing definitions to find common ground or disagreements. It is found that both organization and vendors lack a complete understanding of what information is considered to be CTI, hence more research is needed in order to define CTI. This paper also identified current CTI product and services that include threat intelligence data feeds, threat intelligence standards and tools that being used in CTI. There is an effort by specific industry to shared only relevance threat intelligence data feeds such as Financial Services Information Sharing and Analysis Center (FS-ISAC) that collaborate on critical security threats facing by global financial services sector only. While research and development center such as MITRE working in developing a standards format (e.g.; STIX, TAXII, CybOX) for threat intelligence sharing to solve interoperability issue between threat sharing peers. Based on the review for CTI definition, standards and tools, this paper identifies four research challenges in cyber threat intelligence and analyses contemporary work carried out in each. With an organization flooded with voluminous of threat data, the requirement for qualified threat data analyst to fully utilize CTI and turn the data into actionable intelligence become more important than ever. The data quality is not a new issue but with the growing adoption of CTI, further research in this area is needed.

Smart capacity planning for a better utilisation of NHS wards

Smart capacity planning can support NHS hospitals in achieving optimal utilisation of wards while striking the fine balance between elective and emergency activity at the same time. We propose an actionable business intelligence tool developed with Cambridge's Royal Hospital Papworth, based on the mathematical model of the Queueing Theory. It allows capacity planning not only based on recent bed occupancy, but through the ability to prospectively build occupancy predictions based on different activity scenarios up to one year in advance.

Trend spotting: Using text analysis to model market dynamics

As marketers, we are confronted by an increasingly complex world—one characterized by markets that emerge and evolve at an unprecedented rate. This has led to an increased need for methods that can help researchers translate this complexity and dynamism into actionable intelligence. In the current work, I introduce one such method, and show how it can be applied to the emergence and evolution of the biotechnology industry during the 1990s and early 2000s. I conclude with a discussion of how this method can be applied more broadly to areas like content marketing, trend spotting, and the interface between qualitative and quantitative market research.

Exploring officer views of community policing in counterterrorism

Recently there has been increased emphasis on actionable intelligence in counterterrorism. Building from the process-based model of regulation, police chiefs and scholars generally agree that community policing has promise in this regard. Yet, it is not clear the extent to which police officers concur. Since officers are in a position to implement community policing practices, it is important to understand variants in officer-level support. Using data collected from 741 officers in three departments, this project explores officer-level views of community policing’s utility to address terrorism and more common crimes. Overall, officers view community policing as appropriate to address both common crimes and terrorism. Results suggest that department-level policy itself is not the key driver of support. Rather, an officer’s own experience with community policing and support for the practice in general determine views on community policing in counterterrorism. Results also highlight the importance of comparative research across departments.

An indicator of technical emergence

Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers.

A Knowledge Management Approach to Identify Victims of Human Sex Trafficking

Social media and the interactive Web have enabled human traffickers to lure victims and then sell them faster and in greater safety than ever before. However, these same tools have also enabled investigators in their search for victims and criminals. We used system development action research methodology to create and apply a prototype designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management approach of generating actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. We used the prototype to analyze a data set generated from online ads. We used the results of this process to generate a revised prototype that included the use of machine learning and text mining enhancements. We used the revised prototype to identify potential victims in a second data set. The results of applying the prototypes suggest a viable approach to identifying victims of human sex trafficking in online ads.

Maximizing the Use of Integrated Data Systems: Understanding the Challenges and Advancing Solutions

State and local governments are seeking to use their health, education, and human services administrative data to address major social problems. As they attempt to move forward to develop and use integrated data systems (IDS), they have encountered substantial challenges. The Actionable Intelligence for Social Policy (AISP) team at the University of Pennsylvania targeted four common sets of challenges to IDS use: IDS governance, legal agreements, technology and data security, and data standards. The purpose of this article is to present the results from a year-long convening of four panels of national experts in each of these key topic areas. The results specify the greatest IDS challenges in each of these areas, and develop state-of-the-art responses to innovate the IDS field. It discusses how these solutions promote more effective, efficient, and routine use of IDS that are scalable to advance the IDS field beyond these current limitations in practice.

Big Data and software engineering: prospects for mutual enrichment

Software engineering has evolved over the last 50 years, initially as a response to the so-called software crisis (the problems that organizations had producing quality software systems on time and on budget) of the 1960s and 1970s. Software engineering (SE) has been defined as “the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software”. Software engineering has developed a number of approaches to areas such as software requirements, software design, software testing, and software maintenance. Software development processes such as the waterfall model, incremental development, and the spiral model have been successfully applied to produce high-quality software on time and under budget. More recently, agile software development has gained popularity as an alternative to the more traditional development methods for development of complex systems. Within the last decade or so, advances in technologies such as mobile computing, social networks, cloud computing, and the Internet of things have given rise to massive datasets which have been given the name Big Data (BD). Big Data has been defined as data with 3Vs—high volume, velocity, and variety. Big Data contains so much data that low probability events are captured in the data. These events can be discovered using analytics methods and turned into actionable intelligence which can be used by businesses to gain a competitive advantage. Unfortunately, the very scale of BD often renders inadequate SQL-based relational database systems which have formed the backbone of data intensive systems for the last 30 years, requiring new NoSQL technologies to be effective. In this paper, we will explore how well-established SE technology can be adapted to support successful development of BD projects, as well as how BD techniques can be used to increase the utility of SE processes and techniques. Thus, BD and SE may mutually support and enrich each other.

Defining Novel and Practical Metrics to Assess the Deliverables of Multiparametric Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy

Multiparametric magnetic resonance/ultrasound targeted prostate biopsy is touted as a tool to improve prostate cancer care and yet its true clinical usefulness over transrectal ultrasound guided prostate biopsy has not been systematically analyzed. We introduce 2 metrics to better quantify and report the deliverables of targeted biopsy. We reviewed our prospective database of patients who underwent simultaneous multiparametric magnetic resonance/ultrasound targeted prostate biopsy and transrectal ultrasound guided prostate biopsy. Actionable intelligence metric was defined as the proportion of patients in whom targeted biopsy provided actionable information over transrectal ultrasound guided prostate biopsy. Reduction metric was defined as the proportion of men in whom transrectal ultrasound guided prostate biopsy could have been omitted. We compared metrics in our cohort with those in prior reports. A total of 371 men were included in study. The actionable intelligence and reduction metrics were 22.2% and 83.6% in biopsy naïve cases, 26.7% and 84.2% in prior negative transrectal ultrasound guided prostate biopsy cases, and 24% and 77.5%, respectively, in active surveillance cases. No significant differences were observed among the groups in the actionable intelligence metric and the reduction metric (p = 0.89 and 0.27, respectively). The actionable intelligence metric was 25.0% for PI-RADS™ (Prostate Imaging Reporting and Data System) 3, 27.5% for PI-RADS 4 and 21.7% for PI-RADS 5 lesions (p = 0.73). Transrectal ultrasound guided prostate biopsy could have been avoided in more patients with PI-RADS 3 compared to PI-RADS 4/5 lesions (reduction metric 92.0% vs 76.7%, p<0.01). Our results compare favorably to those of other reported series. The actionable intelligence metric and the reduction metric are novel, clinically relevant quantification metrics to standardize the reporting of multiparametric magnetic resonance/ultrasound targeted prostate biopsy deliverables. Targeted biopsy provides actionable information in about 25% of men. Reduction metric assessment highlights that transrectal ultrasound guided prostate biopsy may only be omitted after carefully considering the risk of missing clinically significant cancers.

Business Strategies Based on Large Sets of Data and Interaction: Business Intelligence

.The dominant perspective in Business Intelligence (BI) projects and applications has been the technological conception, usually focused on the technical-instrumental nature of computing. This conception has avoided the change in the paradigm from a business model based on the use of tangible resources in favour of one based on the exploitation of intangible resources (data, interaction, networks, etc.). This would explain why applied BI projects, remain anchored in the old organization and operational patterns of traditional businesses in most companies. The technological perception of BI gives continuity to the stovepipe activity of companies, both in their management and in their organizational structures, where the impact of interaction as a generator of business opportunities is very limited, and often non-existent; and the effect of large volumes of data as a value generator is reduced to an operational and technical problem. Hence, the importance of considering BI as a new business philosophy that entails new forms of business organization and a new way of management based on the interaction and analysis of large volumes of internally generated data. Our interest is not only to emphasize the nature of the new business philosophy in the application of BI, but to carry out a discussion about the organizational and operational structure of businesses -according to a conception based on interaction and data as generators of business value-, and about actionable intelligence.

Engaging Family Caregivers Translates to Better Health Outcomes and Lower Costs

Background: The changing demographics of the United States coupled with demonstrated savings resulting from innovative home and community-based services (HCBS) programs, has increased the demand for in-home care options for elders and individuals with disabilities. However, there is a lack of understanding as to what it takes for programs serving these populations to be optimally successful. Family caregivers provide valuable insight and real-time actionable intelligence from the home that can be leveraged to help bend the healthcare cost curve. For 12 years, Seniorlink has pioneered an innovative intensive in-home care model, Caregiver Homes, which partners family caregivers with a care team of nurses and social workers. The model provides guidance, financial assistance, and overall support to caregivers. This approach may help the management of expensive healthcare utilization and improve health outcomes. Nationally, this is the first home and community-based model to achieve the National Committee for Quality Assurance’s (NCQA) highest level of Accreditation of Case Management for Long-term Services and Supports (LTSS). Evidence suggests that partnering engaged family caregivers with a care team improves health outcomes and lowers costs for the greater healthcare system, an approach supported by Seniorlink’s novel strategy.

Intelligence in action – Effective strategic behaviors while solving complex problems

Intelligence and complex problem solving (CPS) are two strongly overlapping constructs. In contrast to conventional intelligence tests, CPS-assessments provide process data concerning the interactions between the problem solver and the task. Despite the availability of this data, theory-driven research on students' strategic behaviors during the CPS exploration phase is limited. Consequently, this study investigated how N=495 high-school students applied and adapted two domain-general strategic behaviors, VOTAT (vary-one-thing-at-a-time; Tschirgi, 1980) and NOTAT (vary no-thing-at-a-time), across a set of nine CPS-tasks with different demands (i.e., identifying direct and dynamic effects). Moreover, we examined how intelligence facilitated more effective strategy use. Results of discontinuous latent growth curve modeling showed that (1) students used VOTAT and NOTAT with a higher and increasing relative frequency when these strategic behaviors were effective. In contrast, when the behaviors were ineffective, students used them with a lower and decreasing relative frequency. (2) More intelligent students used VOTAT and NOTAT more often and with a steeper increasing gradient when these behaviors were effective. When the behaviors were ineffective, more intelligent students showed a lower relative frequency, but no steeper gradient decrease. We discuss these findings from the perspective of how intelligence manifests itself in effective strategy use.

Non-linear analysis as a hierarchal classification scheme for vocalizing marine biologics

Many ocean animals use acoustic communications. Breeding, territorial aggression, and hunting actions often include some form, often complex, and can provide actionable intelligence on the vocalizing animals' intent. As a means of considering the linear features of the time series and the corresponding spectral and cyclic frequency content as it pertains to different classes of animals, a hierarchy termed periodic linear, periodic non-linear, and aperiodic non-linear structural vocalization is considered. To demonstrate the application of such a hierarchy, known vocalizations from the red hind grouper (Epinephelus guttatus), the four lined grunter (Pelates quadrilineatus), the spiny lobster (Panulirus argus) are considered and analyzed for grouping according to the hierarchy based on their time, spectral, and cyclic signal content. Finally, a vocalization collected from the invasive Lionfish (Pterois volitans), understood at the time of publication to be the first such recorded instance, is exercised against the hierarchy to show membership identification.

Assessment for Systems Learning: A Holistic Assessment Framework to Support Decision Making Across the Medical Education Continuum.

Viewing health care from a systems perspective-that is, "a collection of different things which, working together, produce a result not achievable by the things alone"-raises awareness of the complex interrelationships involved in meeting society's goals for accessible, cost-effective, high-quality health care. This perspective also emphasizes the far-reaching consequences of changes in one sector of a system on other components' performance. Medical education promotes this holistic view of health care in its curricula and competency requirements for graduation at the undergraduate and graduate training levels. But how completely does medical education apply a systems lens to itself?The continuum of medical training has undergone a series of changes that have moved it more closely to a systems organizational model. Competency assessment criteria have been expanded and more explicitly defined for learners at all levels of training. Outcomes data, in multiple domains, are monitored by external reviewers for program accreditation. However, translating increasing amounts of individual outcomes into actionable intelligence for decision making poses a formidable information management challenge.Assessment in systems is designed to impart a "big picture" of overall system performance through the synthesis, analysis, and interpretation of outcomes data to provide actionable information for continuous systems improvement, innovation, and long-term planning. A systems-based framework is presented for use across the medical education continuum to facilitate timely improvements in individual curriculum components, continuous improvement in overall program performance, and program decision making on changes required to better address society's health care needs.