Interoperability and Information Brokers in Public Safety: An Approach toward Seamless Emergency Communications

When a disaster occurs, the rapid gathering and sharing of crucial information among public safety agencies, emergency response units, and the public can save lives and reduce the scope of the problem; yet, this is seldom achieved. The lack of interoperability hinders effective collaboration across organizational and jurisdictional boundaries. In this article, we propose a general architecture for emergency communications that incorporates (1) an information broker, (2) events and event-driven processes, and (3) interoperability. This general architecture addresses the question of how an information broker can overcome obstacles, breach boundaries for seamless communication, and empower the public to become active participants in emergency communications. Our research is based on qualitative case studies on emergency communications, workshops with public safety agencies, and a comparative analysis of interoperability issues in the European public sector. This article features a conceptual approach toward proposing a way in which public safety agencies can achieve optimal interoperability and thereby enable seamless communication and crowdsourcing in emergency prevention and response

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Distributed Handler Architecture

Thesis (PhD) - Indiana University, Computer Sciences, 2007Over the last couple of decades, distributed systems have been demonstrated an architectural evolvement based on models including client/server, multi-tier, distributed objects, messaging and peer-to-peer. One recent evolutionary step is Service Oriented Architecture (SOA), whose goal is to achieve loose-coupling among the interacting software applications for scalability and interoperability. The SOA model is engendered in Web Services, which provide software platforms to build applications as services and to create seamless and loosely-coupled interactions. Web Services utilize supportive functionalities such as security, reliability, monitoring, logging and so forth. These functionalities are typically provisioned as handlers, which incrementally add new capabilities to the services by building an execution chain. Even though handlers are very important to the service, the way of utilization is very crucial to attain the potential benefits. Every attempt to support a service with an additive functionality increases the chance of having an overwhelmingly crowded chain: this makes Web Service fat. Moreover, a handler may become a bottleneck because of having a comparably higher processing time. In this dissertation, we present Distributed Handler Architecture (DHArch) to provide an efficient, scalable and modular architecture to manage the execution of the handlers. The system distributes the handlers by utilizing a Message Oriented Middleware and orchestrates their execution in an efficient fashion. We also present an empirical evaluation of the system to demonstrate the suitability of this architecture to cope with the issues that exist in the conventional Web Service handler structures

On the Matching of Events in Distributed Brokering Systems

this paper we explore matching, routing and network utilization issues in the context of our research prototype NaradaBrokering [4-12], which provides support for centralized, distributed and peer-to-peer (P2P) interactions [13]. NaradaBrokering has been tested in synchronous and asynchronous applications, including as a media server for audio-video conferencing. Depending on the type of interactions routed and the corresponding matching engines supported, the underlying messaging infrastructure could be viewed either as a distributed light-weight relational or XML database. We discuss the implications, and include results, pertaining to the different matching engines supported within the NaradaBrokering syste