This work demonstrates a wide range of applications that use lambda expressions in SQL. Such injected code snippets form a useful technique required by data mining algorithms to overcome the inflexibility of the SQL language, as the language is limited to predefined aggregations only. Following the ’move computation to the data’ paradigm, we extend SQL lambda functions - also known from common programming languages - for machine- learning tasks.\n\nAs machine-learning relies mostly on gradient descent and tensor data types, we use lambda expressions for clustering and graph-mining algorithms as well as to formulate loss functions and label data. To underline the flexibility gained in SQL, this work demonstrates a main memory database system with integrated lambda expressions accessible through table functions in SQL. By reusing SQL and performing data mining and machine- learning tasks faster than can dedicated tools, this demonstration aims at convincing data scientists of the capabilities of database systems for computational tasks.

The amount of available geospatial data grows at an ever faster pace. This leads to a constantly increasing demand for processing power and storage in order to provide data analysis in a timely manner. At the same time, a lot of geospatial processing is visual and exploratory in nature, thus having bounded precision requirements. We present DeepSPACE, a deep learning-based approximate geospatial query processing engine which combines modest hardware requirements with the ability to answer flexible aggregation queries while keeping the required state to a few hundred KiBs.

We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between columns, even across tables. Our demonstration allows users to define such sketches on the TPC-H and IMDb datasets, monitor the training process, and run ad-hoc queries against trained sketches. We also estimate query cardinalities with HyPer and PostgreSQL to visualize the gains over traditional cardinality estimators.

Nowadays we are drowning in data of various varieties. For all these mixed types and categories of data there exist even more different analysis approaches, often done in single hand-written solutions. We propose to extend HyPer, a main memory database system to a uniform data agent platform following the one system fits all approach for solving a wide variety of data analysis problems. We achieve this by applying a flexible operator concept to a set of various important data exploration algorithms. With that, HyPer solves analytical questions using clustering, classification, association rule mining and graph mining besides standard HTAP (Hybrid Transaction and Analytical Processing) workloads on the same database state. It enables to approach the full variety and volume of HTAP extended for data exploration (HTAPx), and only needs knowledge of already introduced SQL extensions that are automatically optimized by the database's standard optimizer. In this demo we will focus on the benefits and flexibility we create by using the SQL extensions for several well-known mining workloads. In our interactive webinterface for this project named HyPerInsight we demonstrate how HyPer outperforms the best open source competitor Apache Spark in common use cases in social media, geo-data, recommender systems and several other.

In order to handle the database load for web scale applications, the conventional wisdom is that a cluster of database servers and a caching layer are essential. In this work, we argue that modern main memory database systems are often fast enough to consolidate this complex architecture into a single server (plus an additional fail over system). To demonstrate this claim, we design the Monopedia Benchmark , a benchmark for web scale applications modeled after Wikipedia. Using this benchmark, we show that it is indeed possible to run the database workload of one of the largest web sites in the world on a single database server.

In the past few years, massive amounts of location-based data has been captured. Numerous datasets containing user location information are readily available to the public. Analyzing such datasets can lead to fascinating insights into the mobility patterns and behaviors of users. Moreover, in recent times a number of geospatial data-driven companies like Uber, Lyft, and Foursquare have emerged. Real-time analysis of geospatial data is essential and enables an emerging class of applications. Database support for geospatial operations is turning into a necessity instead of a distinct feature provided by only a few databases. Even though a lot of database systems provide geospatial support nowadays, queries often do not consider the most current database state. Geospatial queries are inherently slow given the fact that some of these queries require a couple of geometric computations. Disk-based database systems that do support geospatial datatypes and queries, provide rich features and functions, but they fall behind when performance is considered: specifically if real-time analysis of the latest transactional state is a requirement. In this demonstration, we present HyPerSpace, an extension to the high-performance main-memory database system HyPer developed at the Technical University of Munich, capable of processing geospatial queries with sub-second latencies.