The identification of anomalies and communities of nodes in real-world graphs has applications in widespread domains, from the automatic categorization of wikipedia articles or websites to bank fraud detection. While recent and ongoing research is supplying tools for the analysis of simple unlabeled data, it is still a challenge to find patterns and anomalies in large labeled datasets such as time evolving networks. What do... Read More

The evolution of the Relational Database Management Systems must include not only resources to handle big data, but also complex data (such as images, audios, videos, graphs, multidimensional data, long texts, time series, genetic sequences, etc.), where order-based comparisons are not appropriate, and identity-based comparisons are meaningless. Comparing complex data by similarity stirrers much more meaning from data. However, current... Read More

Running real-time data-intensive applications on Apache Hadoop requires complex architectures to store and query data, typically involving multiple independent systems that are tied together through custom-engineered pipelines. A common pattern is to use a NoSQL engine like Apache HBase for caching and later transformations, the results of which are periodically written to HDFS in one of the popular open columnar... Read More

Dhivya and Zongge will have dry runs for SDM 2017. Dhivya's talk information: Title: The Power of Certainty: A Dirichlet Multinomial Model for Belief Propagation Abstract: Given a friendship network, how certain are we that Smith is a progressive (vs. conservative)? How can we propagate these certainties through the network? While Belief propagation marked the beginning of principled label propagation to classify... Read More

Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration "knobs" that control everything in the system, such as the amount of memory to use for caches and how often data is written to storage. The problem with these knobs is that... Read More

Multi-core in-memory databases promise high-speed online transaction processing.  However, the performance of individual designs suffers when the workload characteristics miss their small sweet spot of a desired contention level, read-write ratio, record size, processing rate, and so forth. Cicada is a single-node multi-core in-memory transactional database with serializability.  To provide high performance under diverse workloads, Cicada reduces overhead and contention... Read More

Relational join is a fundamental data management operation, which highly influences the performance of almost every database query. In this talk, I will show that different workload characteristics and hardware configurations necessitate different main-memory hash join models. Subsequently, I will identify four effective models by which any hash-based join algorithm can be executed. I will characterize the relative merits of... Read More

TensorFlow is an open-source machine learning system, originally developed by the Google Brain team, which operates at large scale and in heterogeneous environments. TensorFlow trains and executes a variety of machine learning models at Google, including deep neural networks for image recognition and machine translation. The system uses dataflow graphs to represent stateful computations, and achieves high performance by mapping... Read More

In-memory database management systems (DBMSs) are a key component of modern on-line analytic processing (OLAP) applications, since they provide low-latency access to large volumes of data. Because disk accesses are no longer the principle bottleneck in such systems, the focus in designing query execution engines has shifted to optimizing CPU performance.  Recent systems have revived an older technique of using... Read More

Managing data sets that are larger than RAM has always been one of the most important tasks for database systems. Traditional systems cache fixed-size pages in an in-memory buffer pool that has complete knowledge of all page accesses and transparently loads/evicts pages from/to disk. While this approach is effective at minimizing the number of I/O operations, it is also one... Read More