John Korah

Modeling complex social scenarios using Culturally Infused Social Networks

Modeling complex real world scenarios require representing and analyzing information from multiple domains including social, economic and political aspects. However, most of the current frameworks in social networks are not generic enough to incorporate multi-domain information or to be applied in different scenarios. Current frameworks also make simplifications in other modeling aspects such as incorporating dynamism and providing multi-scale analyses. Representing culture is critical to truly capture the nuances of various social processes. It also helps to make the framework generic enough to be applied in multiple application domains.We will leverage a novel framework called the Culturally Infused Social Network (CISN) to represent culture using probabilistic reasoning networks called Bayesian Knowledge Bases (BKBs), in representations known as cultural fragments. Cultural fragments model the intent of actors by relating their actions to underlying beliefs and goals. CISN also supports analysis algorithms to make predictions and provide explanations. We validate CISN by simulating the 2006 Somali conflict involving the Islamic Court Union (ICU). The Somali conflict is a complex scenario requiring deep understanding of myriad factors. We focus on analyzing the group stability of ICU, how changing alliance caused conflicts and led to its ultimate demise. We define a metric to measure instability in a group, identify critical factors that led to instability in ICU and provide analyses.

Infusing Social Networks With Culture

Social Network Analysis (SNA) is a powerful tool for analyzing social phenomena that is based on studying how actors are connected or interact with each other. All Social Networks (SNs) are inherently embedded in particular cultures. However, the effect of cultural influence is often missing from SNA techniques. Moreover, to incorporate culture, modeling approaches have to deal with inaccurate, unrealistic, and incomplete cultural data. In order to address this problem, we propose a generic approach to systematically represent culture in the form of relevant factors and relationships, while leveraging relevant social theories, and to infuse them into SNs in order to obtain more realistic and complete analyses. Using two sets of experiments, we validate the effectiveness of our approach and demonstrate the significant advantages obtained through culturally infused SNA.

Large-scale distributed foraging, gathering, and matching for information retrieval: assisting the geospatial intelligence analyst

With the proliferation of online resources, there is an increasing need to effectively and efficiently retrieve data and knowledge from distributed geospatial databases. One of the key challenges of this problem is the fact that geospatial databases are usually large and dynamic. In this paper, we address this problem by developing a large scale distributed intelligent foraging, gathering and matching (I-FGM) framework for massive and dynamic information spaces. We assess the effectiveness of our approach by comparing a prototype I-FGM against two simple controls systems (randomized selection and partially intelligent systems). We designed and employed a medium-sized testbed to get an accurate measure of retrieval precision and recall for each system. The results obtained show that I-FGM retrieves relevant information more quickly than the two other control approaches.

A large-scale distributed framework for information retrieval in large dynamic search spaces

One of the main problems facing human analysts dealing with large amounts of dynamic data is that important information may not be assessed in time to aid the decision making process. We present a novel distributed processing framework called Intelligent Foraging, Gathering and Matching (I-FGM) that addresses this problem by concentrating on resource allocation and adapting to computational needs in real-time. It serves as an umbrella framework in which the various tools and techniques available in information retrieval can be used effectively and efficiently. We implement a prototype of I-FGM and validate it through both empirical studies and theoretical performance analysis.

Efficient Anytime Anywhere Algorithms for Closeness Centrality in Large and Dynamic Graphs

Recent advances in social network analysis methodologies for large (millions of nodes and billions of edges) and dynamic (evolving at different rates) networks have focused on leveraging new high performance architectures, parallel/distributed tools and novel data structures. However, there has been less focus on designing scalable and efficient algorithms to handle the challenges of dynamism in large-scale networks. In our previous work, we presented an overarching anytime anywhere framework for designing parallel and distributed social network analysis algorithms that are scalable to large network sizes and can handle dynamism. A key contribution of our work is to leverage the anytime and anywhere properties of graph analysis problems to design algorithms that can efficiently handle network dynamism by reusing partial results, and by reducing re-computations. In this paper, we present an algorithm for closeness centrality analysis that can handle changes in the network in the form of edge deletions. Using both theoretical analysis and experimental evaluations, we examine the performance of our algorithm with different network sizes and dynamism rates.

Temporal Bayesian Knowledge Bases - Reasoning about uncertainty with temporal constraints

Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.

I-FGM: information retrieval in highly dynamic search spaces

Intelligent foraging, gathering and matching (I-FGM) has been shown to be an effective tool for intelligence analysts who have to deal with large and dynamic search spaces. I-FGM introduced a unique resource allocation strategy based on a partial information processing paradigm which, along with a modular system architecture, makes it a truly novel and comprehensive solution to information retrieval in such search spaces. This paper provides further validation of its performance by studying its behavior while working with highly dynamic databases. Results from earlier experiments were analyzed and important changes have been made in the system parameters to deal with dynamism in the search space. These changes also help in our goal of providing relevant search results quickly and with minimum wastage of computational resources. Experiments have been conducted on I-FGM in a realistic and dynamic simulation environment, and its results are compared with two other control systems. I-FGM clearly outperforms the control systems.

Applying I-FGM to image retrieval and an I-FGM system performance analyses

Intelligent Foraging, Gathering and Matching (I-FGM) combines a unique multi-agent architecture with a novel partial processing paradigm to provide a solution for real-time information retrieval in large and dynamic databases. I-FGM provides a unified framework for combining the results from various heterogeneous databases and seeks to provide easily verifiable performance guarantees. In our previous work, I-FGM had been implemented and validated with experiments on dynamic text data. However, the heterogeneity of search spaces requires our system having the ability to effectively handle various types of data. Besides texts, images are the most significant and fundamental data for information retrieval. In this paper, we extend the I-FGM system to incorporate images in its search spaces using a region-based Wavelet Image Retrieval algorithm called WALRUS. Similar to what we did for text retrieval, we modified the WALRUS algorithm to partially and incrementally extract the regions from an image and measure the similarity value of this image. Based on the obtained partial results, we refine our computational resources by updating the priority values of image documents. Experiments have been conducted on I-FGM system with image retrieval. The results show that I-FGM outperforms its control systems. Also, in this paper we present theoretical analysis of the systems with a focus on performance. Based on probability theory, we provide models and predictions of the average performance of the I-FGM system and its two control systems, as well as the systems without partial processing.

Modeling situational awareness in network centric systems

Modeling Situation awareness (SA) in NCO/NCW environments is inherently challenging due to the complexity of the underlying network, highly dynamic nature of processes, and the need for real time analysis. In this paper, we present a performance model for SA using the Network Centric Operations Performance & Prediction (NCO-PP) framework, an established framework for analyzing and predicting performance of NCO/NCW networks. In this paper, we continue to formulate a realistic model that represents dynamism in both the information and network spaces and also their effects on each other. We validate our model via simulations that compare the performance of SA under various information sharing and filtering paradigms. We provide and define a number of relevant performance metrics for SA and show with experimental results that modeling the dynamism in the network lead to superior SA. We also show that the performance of the SA can be significantly improved with proactive resource allocation that takes into account the real time predictions of the future states of the network and the environment.

I-FGM as a Real Time Information Retrieval Tool for E-Governance

Homeland security and disaster relief are some of the critical areas of E-governance that have to deal with vast amounts of dynamic heterogeneous data. Providing rapid real-time search capabilities for such applications is a challenge. Intelligent Foraging, Gathering, and Matching (I-FGM) is an established framework developed to assist users to find information quickly and effectively by incrementally collecting, processing and matching information nuggets. This framework has been successfully used to develop a distributed, unstructured text retrieval application. In this paper, we apply the I-FGM framework to image collections by using a concept-based image retrieval method. We approach this by incrementally processing images, extracting low-level features and mapping them to higher level concepts. Our empirical evaluation shows that our approach performs competitively compared to some existing approaches in terms of retrieving relevant images while offering the speed advantages of distributed and incremental process and unified framework between text and images.