A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system (RDBMS) to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, learning based on a database management system allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.

We describe an extensive benchmark of platforms available to a user who wants to run a machine learning (ML) inference algorithm over a very large data set, but cannot find an existing implementation and thus must "roll her own" ML code. We have carefully chosen a set of five ML implementation tasks that involve learning relatively complex, hierarchical models. We completed those tasks on four different computational platforms, and using 70,000 hours of Amazon EC2 compute time, we carefully compared running times, tuning requirements, and ease-of-programming of each.

In this paper, we consider the problem of imputation (recovering missing values) in very high-dimensional data with an arbitrary covariance structure. The modern solution to this problem is the Gaussian Markov random field (GMRF). The problem with applying a GMRF to very high-dimensional data imputation is that while the GMRF model itself can be useful even for data having tens of thousands of dimensions, utilizing a GMRF requires access to a sparsified, inverse covariance matrix for the data. Computing this matrix using even state-of-the-art methods is very costly, as it typically requires first estimating the covariance matrix from the data (at a O(nm2) cost for m dimensions and n data points) and then performing a regularized inversion of the estimated covariance matrix, which is also very expensive. This is impractical for even moderately-sized, high-dimensional data sets. In this paper, we propose a very simple alternative to the GMRF called the pair wise Gaussian random field or PGRF for short. The PGRF is a graphical, factor-based model. Unlike traditional Gaussian or GMRF models, a PGRF does not require a covariance or correlation matrix as input. Instead, a PGRF takes as input a set of p (dimension, dimension) pairs for which the user suspects there might be a strong correlation or anti-correlation. This set of pairs defines the graphical structure of the model, with a simple Gaussian factor associated with each of the p (dimension, dimension) pairs. Using this structure, it is easy to perform simultaneous inference and imputation of the model. The key benefit of the approach is that the time required for the PGRF to perform inference is approximately linear with respect to p, where p will typically be much smaller than the number of entries in a m×m covariance or precision matrix.

This paper describes the SimSQL system, which allows for SQLbased specification, simulation, and querying of database-valued Markov chains, i.e., chains whose value at any time step comprises the contents of an entire database. SimSQL extends the earlier Monte Carlo database system (MCDB), which permitted Monte Carlo simulation of static database-valued random variables. Like MCDB, SimSQL uses user-specified "VG functions" to generate the simulated data values that are the building blocks of a simulated database. The enhanced functionality of SimSQL is enabled by the ability to parametrize VG functions using stochastic tables, so that one stochastic database can be used to parametrize the generation of another stochastic database, which can parametrize another, and so on. Other key extensions include the ability to explicitly define recursive versions of a stochastic table and the ability to execute the simulation in a MapReduce environment. We focus on applying SimSQL to Bayesian machine learning.

Online social networking systems are rapidly becoming popular on the Internet for users to share, organize and locate interesting content. However, these systems have increasingly been employed as ideal platforms to spread spam and irrelevant content, abusing the valuable human attention and service resource. We propose a social reputation model to guide users to browse the desirable content. First, we compute the statistical correlation between different users to distinguish various user interests, then, since a user's friends are usually trustworthy and share the similar interest, we further exploit the inherent friend relationships to perform the reliable social enhancements of vote history extension and efficient reputation estimation. In addition to providing a strong incentive for user cooperation, our model can handle the practical problems of inactive users, unpopular content and Sybil attacks effectively and efficiently. Our evaluation on a large-scale realistic network validates our analysis, and shows that our social reputation model can help users find the desirable content in various scenarios with a precision of around 94%.

Online social networking systems are rapidly becoming popular for users to share, organize and locate interesting content. However, these systems have increasingly been employed as platforms to spread spam and irrelevant content, abusing valuable human attention and service resource. In this paper, we propose a social reputation model to guide users to browse desirable content. First, we compute the statistical correlation between different users to distinguish various user interests; then, since a user's friends are usually trustworthy and share similar interest, we further exploit the inherent friend relationships to perform reliable social enhancements of vote history extension and efficient reputation estimation. Our social reputation model provides strong incentives for user cooperation, and moreover, our model can handle practical problems of inactive users, unpopular content and Sybil attacks effectively and efficiently. Our evaluation on a large-scale network validates our analysis, and shows that our social reputation model can help users find the desirable content in various scenarios with a precision of 94%.

To protect our networks against malicious intrusions, we need to evaluate these networks security. Previous works on attack graphs have provided meaningful conclusions on security measurement. However, large attack graphs are still hard to be understood vividly, and few suggestions have been proposed to prevent inside malicious attackers from attacking networks. To address these problems, we propose a novel approach to evaluate network security based on adjacency matrixes, which are constructed from existing attack graphs. With our model, we use gray scale images to show overall security vividly, and get quantitative evaluation scores. Moreover, we create a prioritized list of potential threatening hosts, which can help network administrators to harden network step by step. Analysis on computation cost shows that the upper bound computation cost of our measurement methodology is O(N3), which could be completed in real time. We also give an example to show how to put our methods in practice.

Attack graphs play important roles in analyzing network security vulnerabilities, and previous works have provided meaningful conclusions on the generation and security measurement of attack graphs. However, it is still hard for us to understand attack graphs in a large network, and few suggestions have been proposed to prevent inside malicious attackers from attacking networks. To address these problems, we propose a novel approach to generate and describe attack graphs. Firstly, we construct a two-layer attack graph, where the upper layer is a hosts access graph and the lower layer is composed of some host-pair attack graphs. Compared with previous works, our attack graph has simpler structures, and reaches the best upper bound of computation cost in O(N2). Furthermore, we introduce the adjacency matrix to efficiently evaluate network security, with overall evaluation results presented by gray scale images vividly. Thirdly, by applying prospective damage and important weight factors on key hosts with crucial resources, we can create prioritized lists of potential threatening hosts and stepping stones, both of which can help network administrators to harden network security. Analysis on computation cost shows that the upper bound computation cost of our measurement methodology is O(N3), which could also be completed in real time. Finally, we give some examples to show how to put our methods in practice.

Tagging systems are known to be particularly vulnerable to tag spam. Due to the self-organization and self-maintenance nature of Peer-to-Peer (P2P) overlay networks, users in the P2P tagging systems are more vulnerable to tag spam than the centralized ones. This paper proposes SpamResist, a novel social reliability-based mechanism. For each tag search, SpamResist client groups the search respondents into two categories, namely unfamiliar peers and interacted peers according to the fact whether the client has interacted with such respondents. For the two different categories of peers, the client computes their reliability degrees, and then utilizes these reliability degrees as weights to rank search results. To obtain higher quality search results, we propose a socially-enhanced mechanism, considering social friends can share their previous experience and help improve both the performance and convergence of SpamResist. Finally, the experimental results illustrate that SpamResist can effectively defend against tag spam and work better than the existing search models in P2P tagging systems.

Deceptive behaviors of peers in peer-to-peer (P2P) content sharing systems have become a serious problem due to the features of P2P overlay networks such as anonymity, self-organization, etc. This paper presents Sorcery, a novel active challenge-response mechanism based on the notion that one side of interaction with dominant information can detect whether the other side is telling a lie. To make each client obtain the dominant information, our approach introduces social network to the P2P content sharing system; thus, the client can establish friend-relationships with peers who are either acquaintances in reality or those reliable online friends. Using the confidential voting histories of friends as own dominant information, the client can challenge the content providers with the overlapping votes of both his friends and the content provider, thus detecting whether the content provider is a deceiver. Moreover, Sorcery provides the punishment mechanism which can reduce the impact brought by deceptive behaviors, and our work also discusses some key practical issues. The experimental results illustrate that Sorcery can effectively address the problem of deceptive behaviors, and work better than the existing reputation models.