Connected and autonomous vehicles are changing the future of mobility by providing vehicles connectivity and intelligence for smarter performance. Driver behavior analysis plays an important role in most of these applications. This paper provides a case study using A Distributed Analytics Platform for Telematics (ADAPT) that powers many popular connected car products in the market today. It provides an overview of the platform and some of its supported products. The paper focuses on driver safety data analysis using several novel techniques. It explores image analysis for advanced driver safety using a novel randomized contractive mappings-based technique for efficient onboard driver data analysis. In addition, it explains how social collective incentive mechanisms can be integrated with the driving scoring functions for improving driver safety. The paper discusses market adoption and presents experimental results.

Controling Greenhouse gas (GHG) emissions for minimizing the impact on the environment is one of the major challenges in front of the human civilization. Although future concentrations, damages and costs are unknown, it

This paper describes the MineFleet distributed vehicle performance data mining system designed for commercial fleets. MineFleet analyzes high throughput data streams onboard the vehicle, generates the analytics, sends those to the remote server over the wide-area wireless networks and offers them to the fleet managers using stand-alone and web-based user-interface. The paper describes the overall architecture of the system, business needs, and shares experience from successful large-scale commercial deployments. MineFleet is probably one of the first commercially successful distributed data stream mining systems. This patented technology has been adopted, productized, and commercially offered by many large companies in the mobile resource management and GPS fleet tracking industry. This paper offers an overview of the system and offers a detailed analysis of what made it work.

This paper describes our approach to assured information sharing. The research is being carried out under a MURI 9Multiuniversiyt Research Initiative) project funded by the Air Force Office of Scientific Research (AFOSR). The main objective of our project is: define, design and develop an Assured Information Sharing Lifecycle (AISL) that realizes the DoD's information sharing value chain. In this paper we describe the problem faced by the Department of Defense and our solution to developing an AISL System.

This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and therefore, is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P Web mining application. The proposed optimization-based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.

This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be co-analyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e. converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.

Data intensive peer-to-peer (P2P) networks are finding increasing number of applications. Data mining in such P2P environments is a natural extension. However, common monolithic data mining architectures do not fit well in such environments since they typically require centralizing the distributed data which is usually not practical in a large P2P network. Distributed data mining algorithms that avoid large-scale synchronization or data centralization offer an alternate choice. This paper considers the distributed K-means clustering problem where the data and computing resources are distributed over a large P2P network. It offers two algorithms which produce an approximation of the result produced by the standard centralized K-means clustering algorithm. The first is designed to operate in a dynamic P2P network that can produce clusterings by ldquolocalrdquo synchronization only. The second algorithm uses uniformly sampled peers and provides analytical guarantees regarding the accuracy of clustering on a P2P network. Empirical results show that both the algorithms demonstrate good performance compared to their centralized counterparts at the modest communication cost.

In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each peer) as the \emph{model} of the system. Since the state of the system is constantly changing, it is necessary to keep the models up-to-date. Computing global data mining models e.g. decision trees, k-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient \emph{local} algorithm which can be used to monitor a wide class of data mining models. Then, we use this algorithm as a feedback loop for the monitoring of complex functions of the data such as its k-means clustering. The theoretical claims are corroborated with a thorough experimental analysis.

This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed inferencing, data compaction, data modeling and classification tasks in many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Computing a global regression model from data available at the different peer-nodes using a traditional centralized algorithm for regression can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. This paper proposes a two-step approach to deal with this problem. First, it offers an efficient local distributed algorithm that monitors the “quality” of the current regression model. If the model is outdated, it uses this algorithm as a feedback mechanism for rebuilding the model. The local nature of the monitoring algorithm guarantees low monitoring cost. Experimental results presented in this paper strongly support the theoretical claims.

The inner product measures how closely two feature vectors are related. It is an important primitive for many popular data mining tasks, for example, clustering, classification, correlation computation, and decision tree construction. If the entire data set is available at a single site, then computing the inner product matrix and identifying the top (in terms of magnitude) entries is trivial. However, in many real-world scenarios, data is distributed across many locations and transmitting the data to a central server would be quite communication intensive and not scalable. This paper presents an approximate local algorithm for identifying top-l, inner products among pairs of feature vectors in a large asynchronous distributed environment such as a peer-to-peer (P2P) network. We develop a probabilistic algorithm for this purpose using order statistics and the Hoeffding bound. We present experimental results to show the effectiveness and scalability of the algorithm. Finally, we demonstrate an application of this technique for interest-based community formation in a P2P environment.

The 12 papers in this special section focus on intelligence and security informatics. They are summarized here.

The design, implementation and archiving of large sky surveys is an important part of astronomy research. The Sloan Digital Sky Survey (SDSS), The Two Micron All Sky Survey (2MASS) are some such surveys producing tera bytes of geographically distributed data which need to be stored, analyzed and queried to enable scientific discoveries. In this paper, we describe the architecture of a system for Distributed Exploration of Massive Astronomy Catalogs (DEMAC) which is built on top of the existing National Virtual Observatory environment. We describe distributed algorithms for doing Principal Component Analysis (PCA) using random projection and sampling based techniques. Using the approximate principal components, we develop a distributed outlier detection algorithm which enables identification of data points that deviate sharply from the “correlation structure” of the data. We provide simulation results with data obtained from sky-surveys SDSS and 2MASS.

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an algorithm that (1) is flexible with respect to the outlier definition, (2) works in-network with a communication load proportional to the outcome, and (3) reveals its outcome to all sensors. We examine the algorithm’s performance using simulation with real sensor data streams. Our results demonstrate that the algorithm is accurate and imposes a reasonable communication load and level of power consumption.

This paper presents an algorithm for K-means clustering of data distributed over a large, dynamic network. The network is not assumed to contain any special server nodes (a peer-to-peer network) and is not assumed to be stable either with respect to the topology or the data held by nodes. The algorithm requires only local communication and synchronization at each iteration: nodes communicate and synchronize only with their topologically neighboring nodes. Due to the growing prevalence of peer-to-peer and mobile/wireless sensor networks, data analysis in large, dynamic networks is likely to garner increasing importance in the near future. To our knowledge, our algorithm represents the first K-means algorithm (a common data analysis/mining technique) to be developed for a large dynamic network. We tested our algorithm in a simulated environment of up to 1000 nodes on synthetic data. We examine its behavior in a static environment (no data or network change) and a dynamic environment. Empirical results show the algorithm demonstrates good accuracy (in both the static and dynamic environment) in that the cluster labels produced are very similar to those produced by K-means run on centralized data.

In a large network of computers, wireless sensors, or mobile devices, each of the components (hence, peers) has some data about the global status of the system. Many of the functions of the system, such as routing decisions, search strategies, data cleansing, and the assignment of mutual trust, depend on the global status. Therefore, it is essential that the system be able to detect, and react to, changes in its global status. Computing global predicates in such systems is usually very costly. Mainly because of their scale, and in some cases (e.g., sensor networks) also because of the high cost of communication. The cost further increases when the data changes rapidly (due to state changes, node failure, etc.) and computation has to follow these changes. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient local algorithm which detect when the L2 norm of the average data surpasses a threshold. Then, we use this algorithm as a feedback loop for the monitoring of complex predicates on the data - such as the data's k-means clustering. The efficiency of the L2 algorithm guarantees that so long as the clustering results represent the data (i.e., the data is stationary) few resources are required. When the data undergoes an epoch change - a change in the underlying distribution - and the model no longer represents it, the feedback loop indicates this and the model is rebuilt. Furthermore, the existence of a feedback loop allows using approximate and “best-effort” methods for constructing the model; if an ill-fit model is built the feedback loop would indicate so, and the model would be rebuilt.

This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as bagging, boosting, random forests, and many distributed and data stream mining algorithms. Orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers a technique to construct such trees based on the Fourier transformation of decision trees and eigen-analysis of the ensemble in the Fourier representation. It offers experimental results to document the performance of orthogonal trees on the grounds of accuracy and model complexity.

This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications.

We present an algorithm designed to efficiently construct a decision tree over heterogeneously distributed data without centralizing. We compare our algorithm against a standard centralized decision tree implementation in terms of accuracy as well as the communication complexity. Our experimental results show that by using only 20% of the communication cost necessary to centralize the data we can achieve trees with accuracy at least 80% of the trees produced by the centralized version.

This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as bagging, boosting, random forests and many distributed and data stream mining algorithms. Orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers a technique to construct such trees based on eigen-analysis of the ensemble and offers experimental results to document the performance of orthogonal trees on grounds of accuracy and model complexity.

This paper considers the problem of learning the parameters of a Bayesian network, assuming the structure of the network is given, from a privacy-sensitive dataset that is distributed between multiple parties. For a binary-valued dataset, we show that the count information required to estimate the conditional probabilities in a Bayesian network can be obtained as a solution to a set of linear equations involving some inner product between the relevant different feature vectors. We consider a random projection-based method that was proposed elsewhere to securely compute the inner product (with a modified implementation of that method).

This paper presents an overview of an experimental mobile and distributed data stream mining system that allows real time vehicle-health monitoring and driver characterization. It offers the motivation behind this application, explains the system architecture, discusses many challenges that the project faced, and shares some of the adopted solutions. The main contribution of the paper is our experience in building one of the very early distributed data stream mining systems for wireless applications that performs most of the data analysis related tasks using light-weight on-board computing devices. This paper points out that the distributed data mining technology can play a key role in solving real-life problems in a mobile application environment where computing, storage, power, and communication resources are limited. The paper also illustrates how privacy-preserving distributed data mining can play an important role in this type of applications.

Inductive learning of nonlinear functions plays an important role in constructing predictive models and classifiers from data. We explore a novel randomized approach to construct linear representations of nonlinear functions proposed elsewhere [H. Kargupta (2001)], [H. Kargupta et al., (2002)]. This approach makes use of randomized codebooks, called the genetic code-like transformations (GCTs) for constructing an approximately linear representation of a nonlinear target function. We first derive some of the results presented elsewhere [H. Kargupta et al., (2002)] in a more general context. Next, it investigates different probabilistic and limit properties of GCTs. It also presents several experimental results to demonstrate the potential of this approach.

We present a novel Fourier analysis-based approach to combine, transmit, and visualize decision trees in a mobile environment. Fourier representation of a decision tree has several interesting properties that are particularly useful for mining data streams from small mobile computing devices connected through limited-bandwidth wireless networks. We present algorithms to compute the Fourier spectrum of a decision tree and outlines a technique to construct a decision tree from its Fourier spectrum. It offers a framework to aggregate decision trees in their Fourier representations. It also describes the MobiMine, a mobile data stream mining system, that uses the developed techniques for mining stock-market data from handheld devices.

Defending the safety of an open society from terrorism or other similar threats requires intelligent but careful ways to monitor different types of activities and transactions in the electronic media. Data mining techniques are playing an increasingly important role in sifting through large amount of data in search of useful patterns that might help us in securing our safety. Although the objective of this class of data mining applications is very well justified, they also open up the possibility of misusing personal information by malicious people with access to the sensitive data. This brings up the following question: Can we design data mining techniques that are sensitive to privacy? Several researchers are currently working on a class of data mining algorithms that work without directly accessing the sensitive data in their original form. This paper considers the problem of mining distributed data in a privacy-sensitive manner. It first points out the problems of some of the existing privacy-sensitive data mining techniques that make use of additive random noise to hide sensitive information. Next it briefly reviews some new approaches that make use of random projection matrices for computing statistical aggregates from sensitive data.

Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacy-preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. We question the utility of the random value distortion technique in privacy preservation. We note that random objects (particularly random matrices) have "predictable" structures in the spectral domain and it develops a random matrix-based spectral filtering technique to retrieve original data from the dataset distorted by adding random values. We present the theoretical foundation of this filtering method and extensive experimental results to demonstrate that in many cases random data distortion preserve very little data privacy. We also point out possible avenues for the development of new privacy-preserving data mining techniques like exploiting multiplicative and colored noise for preserving privacy in data mining applications.

We present a collective approach to mining Bayesian networks from distributed heterogenous Web-log data streams. In this approach we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network that models the entire data. We applied this technique to mining multiple data streams, where data centralization is difficult because of large response time and scalability issues. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.

This paper presents a novel Fourier analysis-based technique to aggregate, communicate and visualize decision trees in a mobile environment. A Fourier representation of a decision tree has several useful properties that are particularly useful for mining continuous data streams from small mobile computing devices. This paper presents algorithms to compute the Fourier spectrum of a decision tree and vice versa. It offers a framework to aggregate decision trees in their Fourier representations. It also describes a touchpad/ticker-based approach to visualize decision trees using their Fourier spectrum and an implementation for PDAs.

Spurred by advances in communication technologies, mobile computing and databases that are distributed have become widespread. Such a computing environment involves data that is stored at geographically dispersed locations, and the so-called “slim” computing devices such as palmtops and wearable computers. The decentralized nature of data storage and this new paradigm in computing give rise to several issues, such as security, communication overhead, computational load demands and scalability, that are not adequately addressed by traditional centralized data mining techniques. It is essential that algorithms designed for distributed data mining scenarios mitigate some of these issues. This paper attempts to adapt one centralized data mining technique, decision tree learning, to such an environment. It presents a scalable algorithm that can be used to build decision trees from a distributed, heterogeneous database while minimizing communication overheads. This paper also shows how a decision tree may be represented in terms of its Fourier spectrum. It uses this Fourier spectrum based technique to aggregate decision trees built at the various distributed sites, simplifying the model built during the data mining stage, and notes some additional advantages of the Fourier spectrum approach.

The gene expression process in nature produces different proteins in different cells from different portions of the DNA. Since proteins control almost every important activity in a living organism, at an abstract level, gene expression can be viewed as a process that evaluates the merit or "fitness" of the DNA. This distributed evaluation of the DNA would not be possible without a decomposed representation of the fitness function defined over the DNAs. This paper argues that, unless the living body was provided with such a representation, we have every reason to believe that it must have an efficient mechanism to construct this distributed representation. This paper demonstrates polynomial-time computability of such a representation by proposing a class of efficient algorithms. The main contribution of this paper is two-fold. On the algorithmic side, it offers a way to scale up evolutionary search by detecting the underlying structure of the search space. On the biological side, it proves that the distributed representation of the evolutionary fitness function in gene expression can be computed in polynomial-time. It advances our understanding about the representation construction in gene expression from the perspective of computing. It also presents experimental results supporting the theoretical performance of the proposed algorithms.

No abstract available.

The paper introduces the gene expression messy genetic algorithm (GEMGA)-a new generation of messy GAs that may find many applications in financial engineering. Unlike other existing blackbox optimization algorithms, GEMGA directly searches for relations among the members of the search space. The GEMGA is an O(|/spl Lambda/|/sup k/(l+k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995) (problems that can be solved by considering no higher than order-k relations) in sequence representation of length L and alphabet set /spl Lambda/. The GEMGA is designed based on the alternate perspective of natural evolution proposed by the SEARCH framework (Kargupta, 1995) that emphasizes the role of gene expression. The paper also presents the test results for large multimodal problems and identifies possible applications to financial engineering.