SimRank is a similarity measure for graph nodes that has numerous applications in practice. Scalable SimRank computation has been the subject of extensive research for more than a decade, and yet, none of the existing solutions can efficiently derive ...

The CPU cache performance is one of the key issues to efficiency in database systems. It is reported that cache miss latency takes a half of the execution time in database systems. To improve the CPU cache performance, there are studies to support ...

Histograms are implemented and used in any database system, usually defined on a single-column of a database table. However, one of the most desired statistical data in such systems are statistics on the correlation among columns. In this paper we ...

In recent years we have witnessed a surging interest in developing Big Graph processing systems. To date, tens of Big Graph systems have been proposed. This tutorial provides a timely and comprehensive review of existing Big Graph systems, and ...

Data analysis may be a difficult task, especially for non-expert users, as it requires deep understanding of the investigated domain and the particular context. In this demo we present REACT, a system that hooks to the analysis UI and provides the users ...

An increasing number of applications in all aspects of society rely on data. Despite the long line of research in data cleaning and repairs, data correctness has been an elusive goal. Errors in the data can be extremely disruptive, and are detrimental ...

Real-world graphs are not always publicly available or sometimes do not meet specific research requirements. These challenges call for generating synthetic networks that follow properties of the real-world networks. Barabási-Albert (BA) is a well-known ...

The increasing ability to collect data from urban environments, coupled with a push towards openness by governments, has resulted in the availability of numerous spatio-temporal data sets covering diverse aspects of a city. Discovering relationships ...

In this paper, we study the problem of subgraph matching that extracts all subgraph isomorphic embeddings of a query graph q in a large data graph G. The existing algorithms for subgraph matching follow Ullmann's backtracking approach; that is, ...

A graph stream, which refers to the graph with edges being updated sequentially in a form of a stream, has important applications in cyber security and social networks. Due to the sheer volume and highly dynamic nature of graph streams, the practical ...

Influence Maximization (IM), that seeks a small set of key users who spread the influence widely into the network, is a core problem in multiple domains. It finds applications in viral marketing, epidemic control, and assessing cascading failures within ...

DeepDive is a system for extracting relational databases from dark data: the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data --- scientific ...

R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the R runtime is single threaded and can only process data ...

We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying ...

We present a system that approximates the answer to complex ad-hoc queries in big-data clusters by injecting samplers on-the-fly and without requiring pre-existing samples. Improvements can be substantial when big-data queries take multiple passes over ...

Billion-node graphs are rapidly growing in size in many applications such as online social networks. Most graph algorithms generate a large number of messages during iterative computations. Vertex-centric distributed systems usually store graph data and ...

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We ...

A key operation in network analysis is the discovery of cohesive subgraphs. The notion of $k$-truss has gained considerable popularity in this regard, based on its rich structure and efficient computability. However, many complex networks such as social,...

RFID is widely used for object tracking in indoor environments, e.g., airport baggage tracking. Analyzing RFID data offers insight into the underlying tracking systems as well as the associated business processes. However, the inherent uncertainty in ...