SIGMOD

The topics of private data analysis and streaming data management have both been separately the focus of much study within the data management community for many years. However, more recently there have been studies which bring these two previously ...

We consider the problem of computing differentially private approximate histograms and heavy hitters in a stream of elements. In the non-private setting, this is often done using the sketch of Misra and Gries [Science of Computer Programming, 1982]. Chan,...

Among the ways a database management system adds value, is the transaction abstraction, where the application coder can group together multiple data accesses that collectively perform one meaningful real-world activity. The platform will provide the "...

A serializable concurrency control mechanism ensures consistency for OLTP systems at the expense of a reduced transaction throughput. A DBMS therefore usually offers the possibility to allocate lower isolation levels for some transactions when it is safe ...

Most queries access data from more than one relation, which makes joins between relations an extremely common operation. In many cases the execution time of a query is dominated by the processing of the involved joins. This observation has led to a wide ...

Over the last decade, worst-case optimal join (WCOJ) algorithms have emerged as a new paradigm for one of the most fundamental challenges in query processing: computing joins efficiently. Such an algorithm can be asymptotically faster than traditional ...

When interactively working with data, query latency is very important. In particular when ad-hoc queries are written in an explorative manner, it is essential to quickly get feedback in order to refine and correct the query based upon result values. This ...

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster response times than the hardware allows for exact query answering. However, existing AQP methods impose steep performance penalties as workload unpredictability ...

Data integration has been a long-standing challenge for data management. It has recently received significant attention due to at least three main reasons. First, many data science projects require integrating data from disparate sources before analysis ...

Data matching, which decides whether two data elements (e.g., string, tuple, column, or knowledge graph entity) are the "same" (a.k.a. a match), is a key concept in data integration. The widely used practice is to build task-specific or even dataset-...

Nearly all of the world's population now uses online services that request personal information, covering almost every aspect of our lives. The abundance of personal data in digital form has brought incredible benefits to end users, enabling them to ...

Through legislation and technical advances users gain more control over how their data is processed, and they expect online services to respect their privacy choices and preferences. However, data may be processed for many different purposes by several ...

Synthetic data is a vital substitute for real sensitive personal data in supporting social science research and policy studies. Extensive prior research has delved into various models for generating synthetic data, from traditional statistical approaches ...

Differential privacy (DP) data synthesizers are increasingly proposed to afford public release of sensitive information, offering theoretical guarantees for privacy (and, in some cases, utility), but limited empirical evidence of utility in practical ...

By now, it is widely-accepted folk wisdom that "half of the time in any data analysis project is spent wrangling the data". Analytic algorithms and tools-built on mathematical foundations of matrices and relations-require their data to be lined up in ...

Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild"...

DBSP (Data Base Stream Processing) is a simple yet expressive language for stream computation that draws inspiration from DSP (Digital Signal Processing). In DBSP, stream computation is expressed using circuits of stream operators whose input and output ...

We describe DBSP, a framework for incremental computation. Incremental computations repeatedly evaluate a function on some input values that are "changing". The goal of an efficient implementation is to "reuse" previously computed results. Ideally, when ...