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An important problem in the area of homeland security is to identify abnormal or suspicious entities in large data sets. Although there are methods from data mining and social network analysis focusing on finding patterns or central nodes from networks or numerical data sets, there has been little work aimed at discovering abnormal instances in large complex semantic graphs, whose nodes are richly connected with many different types of links. In this paper, we describe a novel unsupervised framework to identify such instances. Besides discovering abnormal instances, we believe that to complete the process, a system has to also provide users with understandable explanations for its findings. Therefore, in the second part of the paper, we describe an explanation mechanism to automatically generate human-understandable explanations for the discovered results. To evaluate our discovery and explanation systems, we perform experiments on several different semantic graphs. The results show that our discovery system outperforms state-of-the-art unsupervised network algorithms used to analyze the 9/11 terrorist network and other graph-based outlier detection algorithms by a significant margin. Additionally, the human study we conducted demonstrates that our explanation system, which provides natural language explanations for the system's findings, allowed human subjects to perform complex data analysis in a much more efficient and accurate manner. [ABSTRACT FROM AUTHOR]

Impact-targeted activities are rare but they may have a significant impact on the society. For example, isolated terrorism activities may lead to a disastrous event, threatening the national security. Similar issues can also be seen in many other areas. Therefore, it is important to identify such particular activities before they lead to having a significant impact to the world. However, it is challenging to mine impact-targeted activity patterns due to their imbalanced structure. This paper develops techniques for discovering such activity patterns. First, the complexities of mining imbalanced impact-targeted activities are analyzed. We then discuss strategies for constructing impact-targeted activity sequences. Algorithms are developed to mine frequent positive-impact-oriented (P → T¯) and negative-impact-oriented (P → T) activity patterns, sequential impact-contrasted activity patterns (P is frequently associated with both patterns P → T and P → T¯ in separated data sets), and sequential impact-reversed activity patterns (both P → T and PQ → T¯ are frequent). Activity impact modeling is also studied to quantify the pattern impact on business outcomes. Social security debt-related activity data is used to test the proposed approaches. The outcomes show that they are promising for information and security informatics (ISI) applications to identify impact-targeted activity patterns in imbalanced data. [ABSTRACT FROM AUTHOR]

Given a transportation network having source nodes with evacuees and destination nodes, we want to find a contraflow network configuration (that is, ideal direction for each edge) to minimize the evacuation time. Contraflow lane reversal is considered a potential remedy to reduce congestion during evacuations in the context of homeland security and natural disasters (for example, hurricanes). This problem is computationally challenging because of the very large search space and the expense of calculating the evacuation time on a given network. To our knowledge, this paper presents the first macroscopic approaches for the solution of a contraflow network reconfiguration incorporating road capacity constraints, multiple sources, congestion, and scalability. We formally define the contraflow problem based on graph theory and provide a framework of computational structure to classify our approaches. A Greedy heuristic is designed to produce high-quality solutions with significant performance. A Bottleneck Relief heuristic is developed to deal with large numbers of evacuees. We evaluate the proposed approaches both analytically and experimentally using real-world data sets. Experimental results show that our contraflow approaches can reduce the evacuation time by 40 percent or more. [ABSTRACT FROM AUTHOR]

In this paper, we leverage network coding (NC) to reduce the bandwidth consumption for data broadcast (DB). We use the concept of mixing in a different way from the traditional NC. Traditional NC mixes all data items together. However, we mix each data item from only some data items according to the queried and stored data of receivers. Therefore, each receiver in our approach is required to receive fewer coded data to decode the required information, and the sender, thereby, is able to broadcast fewer data items. We formulate an optimization problem with integer-linear programming to minimize the bandwidth consumption in DB. We prove that the problem is NP-hard and design an approximation algorithm that can be implemented in the data server. In addition, we show that different mixings of data items lead to different decoding costs for receivers. We design an algorithm to optimally code the data items with the minimum decoding cost. [ABSTRACT FROM AUTHOR]

Checking data possession in networked information systems such as those related to critical infrastructures (power facilities, airports, data vaults, defense systems, and so forth) is a matter of crucial importance. Remote data possession checking protocols permit checking that a remote server can access an uncorrupted file in such a way that the verifier does not need to know beforehand the entire file that is being verified. Unfortunately, current protocols only allow a limited number of successive verifications or are impractical from the computational point of view. In this paper, we present a new remote data possession checking protocol such that 1) it allows an unlimited number of file integrity verifications and 2) its maximum running time can be chosen at set-up time and traded off against storage at the verifier. [ABSTRACT FROM AUTHOR]

Many applications require a randomized ordering of input data. Examples include algorithms for online aggregation, data mining, and various randomized algorithms. Most existing work seems to assume that accessing the records from a large database in a randomized order is not a difficult problem. However, it turns out to be extremely difficult in practice. Using existing methods, randomization is either extremely expensive at the front end (as data are loaded), or at the back end (as data are queried). This paper presents a simple file structure which supports both efficient, online random shuffling of a large database, as well as efficient online sampling or randomization Of the database when it is queried. The key innovation of our method is the introduction of a small degree of carefully controlled, rigorously monitored nonrandomness into the file. [ABSTRACT FROM AUTHOR]