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The circuit structure optimizationed with the traditional method is often difficult to meet the complex and changeable design requirements. In this paper the A3C algorithm has been applied to integrate strategy learning and value learning for the circuit ...

Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique ...

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, ...

The Social Network Benchmark's Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in ...

As graph databases become widespread, the International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) have approved a project to create GQL, a standard property graph query language. This complements the SQL/...

We describe how GSQL, TigerGraph's graph query language, supports the specification of aggregation in graph analytics. GSQL makes several unique design decisions with respect to both the expressive power and the evaluation complexity of the specified ...

Antijoin cardinality estimation is among a handful of problems that has eluded accurate efficient solutions amenable to implementation in relational query optimizers. Given the widespread use of antijoin and subset-based queries in analytical workloads ...

This paper gives an overview of Turn Data Management Platform (DMP). We explain the purpose of this type of platforms, and show how it is positioned in the current digital advertising ecosystem. We also provide a detailed description of the key ...

The application of stochastic models and analysis techniques to large datasets is now commonplace. Unfortunately, in practice this usually means extracting data from a database system into an external tool (such as SAS, R, Arena, or Matlab), and then ...

Given a spatial dataset placed on an n ×n grid, our goal is to find the rectangular regions within which subsets of the dataset exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a ...

Given a spatial data set placed on an n x n grid, our goal is to find the rectangular regions within which subsets of the data set exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a ...

For a large number of data management problems, it would be very useful to be able to obtain a few samples from a data set, and to use the samples to guess the largest (or smallest) value in the entire data set. Min/max online aggregation, Top-k query ...

We demonstrate our prototype of the DBO database system. DBO is designed to facilitate scalable analytic processing over large data archives. DBO's analytic processing performance is competitive with other database systems; however, unlike any other ...

To deal with data uncertainty, existing probabilistic database systems augment tuples with attribute-level or tuple-level probability values, which are loaded into the database along with the data itself. This approach can severely limit the system's ...

In general, the task of detecting anomalies is to find the most anomalous points or subset of points from a given data set according to a user-defined score function. In order to give users the freedom to try different score functions during data ...

For a large number of data management problems, it would be very useful to be able to obtain a few samples from a data set, and to use the samples to guess the largest (or smallest) value in the entire data set. Min/max online aggregation, top-k query ...

This paper deals with detecting change of distribution in multi-dimensional data sets. For a given baseline data set and a set of newly observed data points, we define a statistical test called the density test for deciding if the observed data points ...

When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact ...

An effective approach to detecting anomalous points in a data set is distance-based outlier detection. This paper describes a simple sampling algorithm to effciently detect distance-based outliers in domains where each and every distance computation is ...