Tingjian Ge

Top- k queries on uncertain data: on score distribution and typical answers

Uncertain data arises in a number of domains, including data integration and sensor networks. Top-k queries that rank results according to some user-defined score are an important tool for exploring large uncertain data sets. As several recent papers have observed, the semantics of top-k queries on uncertain data can be ambiguous due to tradeoffs between reporting high-scoring tuples and tuples with a high probability of being in the resulting data set. In this paper, we demonstrate the need to present the score distribution of top-k vectors to allow the user to choose between results along this score-probability dimensions. One option would be to display the complete distribution of all potential top-k tuple vectors, but this set is too large to compute. Instead, we propose to provide a number of typical vectors that effectively sample this distribution. We propose efficient algorithms to compute these vectors. We also extend the semantics and algorithms to the scenario of score ties, which is not dealt with in the previous work in the area. Our work includes a systematic empirical study on both real dataset and synthetic datasets.

Collaboration in multicloud computing environments: Framework and security issues

A proposed proxy-based multicloud computing framework allows dynamic, on-the-fly collaborations and resource sharing among cloud-based services, addressing trust, policy, and privacy issues without preestablished collaboration agreements or standardized interfaces.

Fast, Secure Encryption for Indexing in a Column-Oriented DBMS

Networked information systems require strong security guarantees because of the new threats that they face. Various forms of encryption have been proposed to deal with this problem. In a database system, there are often two contradictory goals: security of the encryption and fast performance of queries. There have been a number of proposals of database encryption schemes to facilitate queries on encrypted columns. Order-preserving encryption techniques are well-suited for databases since they support a simple, and efficient way to build indices. However, as we will show, they are insecure under straightforward attack scenarios. We propose a new light-weight database encryption scheme (called FCE) for column stores in data warehouses with trusted servers. The low decryption overhead of FCE makes comparisons of ciphertexts and hence indexing operations very fast. Since it is hard to use classical security definitions in cryptography to prove the security of any existing symmetric encryption scheme, we propose a relaxed measure of security, called INFO-CPA-DB. INFO-CPA-DB is based on a well-established security definition in cryptography and relaxes it using information theoretic concepts. Using INFO-CPA-DB, we give strong evidence that FCE is as secure as any underlying block cipher (yet more efficient than using the block cipher itself). Using the same security measure we also show the inherent insecurity of any order preserving encryption scheme under straightforward attack scenarios. We discuss indexing techniques based on FCE as well.

JR: Flexible distributed programming in an extended Java

Java provides a clean object-oriented programming model and allows for inherently system-independent programs. Unfortunately, Java has a limited concurrency model, providing only threads and remote method invocation (RMI).The JR programming language extends Java to provide a rich concurrency model, based on that of SR. JR provides dynamic remote virtual machine creation, dynamic remote object creation, remote method invocation, asynchronous communication, rendezvous, and dynamic process creation. JRs concurrency model stems from the addition of operations (a generalization of procedures) and JR supports the redefinition of operations through inheritance. JR programs are written in an extended Java and then translated into standard Java programs. The JR run-time support system is also written in standard Java.This paper describes the JR programming language and its implementation. Some initial measurements of the performance of the implementation are also included.

Single-Component Versus Multicomponent Dietary Goals for the Metabolic Syndrome: A Randomized Trial

Background Few studies have compared diets to determine if a program focused upon one dietary change results in collateral effects on other untargeted healthy diet components.

Event pattern matching over graph streams

A graph is a fundamental and general data structure underlying all data applications. Many applications today call for the management and query capabilities directly on graphs. Real time graph streams, as seen in road networks, social and communication networks, and web requests, are such applications. Event pattern matching requires the awareness of graph structures, which is different from traditional complex event processing. It also requires a focus on the dynamicity of the graph, time order constraints in patterns, and online query processing, which deviates significantly from previous work on subgraph matching as well. We study the semantics and efficient online algorithms for this important and intriguing problem, and evaluate our approaches with extensive experiments over real world datasets in four different domains.

JR: flexible distributed programming in an extended Java

Java provides a clean object-oriented programming model and allows for inherently system-independent programs. Unfortunately, Java has a limited concurrency model, providing only threads and remote method invocation (RMI). The JR programming language extends Java to provide a rich concurrency model. JR provides dynamic remote virtual machine creation, dynamic remote object creation, remote method invocation, asynchronous communication, rendezvous, and dynamic process creation. JR programs are written in an extended Java and then translated into standard Java programs. The JR run-time support system is also written in standard Java. This paper describes the JR programming language and its implementation. Some initial measurements of the performance of the implementation are also included.

A skip-list approach for efficiently processing forecasting queries

Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support prediction queries over time series data. Prediction relies on model building which can be too expensive to be practical if it is based on a large number of data points. We propose to use statistical tests of hypotheses to choose a proper subset of data points to use for a given prediction query interval. This involves two steps: choosing a proper history length and choosing the number of data points to use within this history. Further, we use an I/O conscious skip list data structure to provide samples of the original data set. Based on the statistics collected for a query workload, which we model as a probability mass function (PMF) over query intervals, we devise a randomized algorithm that selects a set of pre-built models (PMs) to construct, subject to some maintenance cost constraint when there are updates. Given this set of PMs, we discuss interesting query processing strategies for not only point queries, but also range, aggregation, and JOIN queries. We conduct a comprehensive empirical study on real world datasets to verify the effectiveness of our approaches and algorithms.

Aroma: A New Data Protection Method with Differential Privacy and Accurate Query Answering

We propose a new local data perturbation method called Aroma. We first show that Aroma is sound in its privacy protection. For that, we devise a realistic privacy game, called the exposure test. We prove that the αβ algorithm, a previously proposed method that is most closely related to Aroma, performs poorly under the exposure test and fails to provide sufficient privacy in practice. Moreover, any data protection method that satisfies ε-differential privacy will succeed in the test. By proving that Aroma satisfies ε-differential privacy, we show that Aroma offers strong privacy protection. We then demonstrate the utility of Aroma by proving that its estimator has significantly smaller errors than the previous state-of-the-art algorithms such as αβ, AM, and FRAPP. We carry out a systematic empirical study using real-world data to evaluate Aroma, which shows its clear advantages over previous methods.

Online windowed subsequence matching over probabilistic sequences

Windowed subsequence matching over deterministic strings has been studied in previous work in the contexts of knowledge discovery, data mining, and molecular biology. However, we observe that in these applications, as well as in data stream monitoring, complex event processing, and time series data processing in which streams can be mapped to strings, the strings are often noisy and probabilistic. We study this problem in the online setting where efficiency is paramount. We first formulate the query semantics, and propose an exact algorithm. Then we propose a randomized approximation algorithm that is faster and, in the mean time, provably accurate. Moreover, we devise a filtering algorithm to further enhance the efficiency with an optimization technique that is adaptive to sequence stream contents. Finally, we propose algorithms for patterns with negations. In order to verify the algorithms, we conduct a systematic empirical study using three real datasets and some synthetic datasets.