Strange bedfellows? Keyword and conceptual search unite to make sense of relevant ESI in electronic discovery

In the brief history of electronic discovery, the latter part of the twentieth century witnessed the demise of paper by a digital hero that emancipated the content of paper documents with OCR and TIFF. This technology added a third dimension to the realm of 2D paper document review and production that lead to a sea change in discovery methods. By many accounts what we have before us is a three-stage evolution from paper to digital to clustering in order to overcome the problems of volume and complexity of ESI. The intent of this position paper is to describe the development of the digital hero and methodology that is emancipating the content and context of ESI – conceptual search that spans file formats, languages and technique, and includes keyword search on a common, shared index

Big Data solutions for law enforcement

Big Data, the data too large and complex for most current information infrastructure to store and analyze, has changed every sector in government and industry. Today’s sensors and devices produce an overwhelming amount of information that is often unstructured, and solutions developed to handle Big Data now allowing us to track more information and run more complex analytics to gain a level of insight once thought impossible. The dominant Big Data solution is the Apache Hadoop ecosystem which provides an open source platform for reliable, scalable, distributed computing on commodity hardware. Hadoop has exploded in the private sector and is the back end to many of the leading Web 2.0 companies and services. Hadoop also has a growing footprint in government, with numerous Hadoop clusters run by the Departments of Defense and Energy, as well as smaller deployments by other agencies. One sector currently exploring Hadoop is law enforcement. Big Data analysis has already been highly effective in law enforcement and can make police departments more effective, accountable, efficient, and proactive. As Hadoop continues to spread through law enforcement agencies, it has the potential to permanently change the way policing is practiced and administered

Tactical ISR/C2 Integration with AI/ML Augmentation

NPS NRP Project PresentationNAVPLAN 2021 specifies Distributed Maritime Operations (DMO) with a tactical grid to connect distributed nodes with processing at the tactical edge to include Artificial Intelligence/Machine Learning (AI/ML) in support of Expeditionary Advanced Base Operations (EABO) and Littoral Operations in a Contested Environment (LOCE). Joint All-Domain Command and Control (JADC2) is the concept for sensor integration. However, Intelligence, Surveillance and Reconnaissance (ISR) and Command and Control (C2) hardware and software have yet to be fully defined, tools integrated, and configurations tested. This project evaluates options for ISR and C2 integration into a Common Operational Picture (COP) with AI/ML for decision support on tactical clouds in support of DMO, EABO, LOCE and JADC2 objectives.Commander, Naval Surface Forces (CNSF)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

ClouNS - A Cloud-native Application Reference Model for Enterprise Architects

The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies