With the emergence of the Internet of Things (IoT), time series data has become ubiquitous in our daily life. Making sense of time series is a topic of great interest in many domains. Existing time series analysis applications generally assume or even require perfect time series (i.e. regular time intervals without unknown values), but real-world time series are rarely so neat. They often contain "holes" of different sizes (i.e., single missing values, or blocks of consecutive missing values) due to some failures or irregular time intervals. Hence, missing value recovery is a prerequisite for many time series analysis applications. In this demo, we present RecovDB, a relational database system enhanced with advanced matrix decomposition technology for missing blocks recovery. This demo will show the main features of RecovDB that are important for today's time series analysis but are lacking in state-of-the-art technologies: i) recovering large missing blocks in multiple time series at once; ii) achieving high recovery accuracy by benefiting from different correlations across time series; iii) maintaining recovery accuracy under increasing size of missing blocks; iv) maintaining recovery efficiency with increasing time series' lengths and the number of time series; and iv) supporting all these features while being parameter-free. In this paper, we also compare the efficiency and accuracy of RecovDB against state-of-the-art recovery systems.

Scale-out of big data analytics applications often does not pay off due to the poor performance in response time and the increasing bill due to a longer execution time on a resource limited machine. To enable a stable DBMS workload environment it helps to maintain several virtual machines with difference resource configurations (CPU, memory, disk, etc) hosting part of the database, so that users can send their tasks to those machines that have the best price/performance characteristics. This, however, requires a method to decide which VM should be used for a given query. When choosing the VM, the memory usage of a query is a particularly important factor, especially for the main-memory (optimised) DBMSs which are generally used for analytical queries today. In this paper, we introduce MALCOM, a memory footprint predictor for queries based on resilient intermediates in MonetDB. Unlike traditional cost-based approaches, MALCOM uses an empirical approach (i.e. using the memory usage information of queries executed in the past) to incrementally update its model to improve its predictions. Our preliminary experiment results show that this approach is robust against varying data distributions.

This talk first shows how an in-database machine learning system has been realised by a seamless integration of MonetDB (an open-source analytical columnar DBMS) and TensorFlow (an open-source machine learning library). Then we show with an example application of entity linking using neural embeddings the potential of this integration.

Despite their proliferation as a dominant computing paradigm, cloud computing systems lack effective mechanisms to manage their vast amounts of resources efficiently. Resources are stranded and fragmented, ultimately limiting cloud systems' applicability to large classes of critical applications that pose non-moderate resource demands. Eliminating current technological barriers of actual fluidity and scalability of cloud resources is essential to strengthen cloud computing's role as a critical cornerstone for the digital economy. ACTiCLOUD proposes a novel cloud architecture that breaks the existing scale-up and share-nothing barriers and enables the holistic management of physical resources both at the local cloud site and at distributed levels. Specifically, it makes advancements in the cloud resource management stacks by extending state-of-the-art hypervisor technology beyond the physical server boundary and localized cloud management system to provide a holistic resource management within a rack, within a site, and across distributed cloud sites. On top of this, ACTiCLOUD will adapt and optimize system libraries and runtimes (e.g., JVM) as well as ACTiCLOUD-native applications, which are extremely demanding, and critical classes of applications that currently face severe difficulties in matching their resource requirements to state-of-the-art cloud offerings.

Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of experimental data using database technologies. To bridge the gap between the needs of the Data-Intensive Research fields and the current DBMS technologies, we have introduced SciQL (pronounced as 'cycle'). SciQL is the first SQL-based declarative query language for scientific applications with both tables and arrays as first class citizens. It provides a seamless symbiosis of array-, set- and sequence- interpretations. A key innovation is the extension of value-based grouping of SQL:2003 with structural grouping, i.e., group array elements based on their positions. This leads to a generalisation of window-based query processing with wide applicability in science domains.

Efficient management and exploration of high-volume scientific file repositories have become pivotal for advancement in science. We propose to demonstrate the Data Vault, an extension of the database system architecture that transparently opens scientific file repositories for efficient in-database processing and exploration.

Both scientific data and business data have analytical needs. Analysis takes place after a scientific data warehouse is eagerly filled with all data from external data sources (repositories). This is similar to the initial loading stage of Extract, Transform, and Load (ETL) processes that drive business intelligence. ETL can also help scientific data analysis. However, the initial loading is a time and resource consuming operation. It might not be entirely necessary, e.g. if the user is interested in only a subset of the data. We propose to demonstrate Lazy ETL, a technique to lower costs for initial loading. With it, ETL is integrated into the query processing of the scientific data warehouse. For a query, only the required data items are extracted, transformed, and loaded transparently on-the-fly. The demo is built around concrete implementations of Lazy ETL for seismic data analysis. The seismic data warehouse is ready for query processing, without waiting for long initial loading. The audience fires analytical queries to observe the internal mechanisms and modifications that realize each of the steps; lazy extraction, transformation, and loading.

TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery and exploitation of knowledge that is hidden in them. TELEIOS builds on scientific database technologies (array databases, SciQL, data vaults) and Semantic Web technologies (stRDF and stSPARQL) implemented on top of a state of the art column store database system (MonetDB). We demonstrate a first prototype of the TELEIOS Virtual Earth Observatory (VEO) architecture, using a forest fire monitoring application as example.

Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of experimental data using database technologies. To bridge the gap between the needs of the Data-Intensive Research fields and the current DBMS technologies, we propose SciQL (pronounced as 'cycle'), the first SQL-based query language for scientific applications with both tables and arrays as first class citizens. It provides a seamless symbiosis of array-, set- and sequence-interpretations. A key innovation is the extension of value-based grouping of SQL:2003 with structural grouping, i.e., fixed-sized and unbounded groups based on explicit relationships between elements positions. This leads to a generalisation of window-based query processing with wide applicability in science domains. This paper describes the main language features of SciQL and illustrates it using time-series concepts.

We investigate techniques to automatically decompose any XQuery query—including updating queries specified by the XQuery Update Facility (XQUF)—into subqueries, that can be executed near their data sources, i.e., function-shipping. The main challenge addressed here is to ensure that the decomposed queries properly respect XML node identity and preserve structural properties, when (parts of) XML nodes are sent over the network, effectively copying them. We start by precisely characterizing the conditions, under which pass-by-value parameter passing causes semantic differences between remote execution of an XQuery expression and its local execution. We then formulate a conservative strategy that effectively avoids decomposition in such cases. To broaden the possibilities of query distribution, we extend the pass-by-value semantics to a pass-by-fragment semantics, which keeps better track of node identities and structural properties. The pass-by-fragment semantics is subsequently refined to a pass-by-projection semantics by means of a novel runtime XML projection technique, which safely eliminates most semantic differences between the local and remote execution of an XQuery expression, and strongly reduces message sizes. Finally, we discuss how these techniques can be used for updating queries, both under the standard W3C XQUF specification, as well as under an extended semantics that allows to update remote documents. The proposed techniques are implemented in XRPC, a simple yet efficient XQuery extension that enables function-shipping by adding a Remote Procedure Call mechanism to XQuery. Experiments on MonetDB/XQuery establish the performance potential of our XQuery decomposition techniques.

We investigate techniques to automatically decompose any XQuery query into subqueries, that can be executed near their data sources; i.e., function-shipping. In this scenario, the subqueries being executed remotely may have XML node-valued parameters or results, that must be shipped in some way. The main challenge addressed here is to ensure that the decomposed queries properly respect XML node identity and preserve structural properties, when (parts of) XML nodes are sent over the network, effectively copying them. We start by precisely characterizing the conditions, under which pass-by-value parameter passing causes semantic differences between remote execution of an XQuery expression and its local execution. We then formulate a conservative strategy that effectively avoids decomposition in such cases. To broaden the possibilities of query distribution, we extend the pass-by value semantics to a pass-by-fragment semantics, which keeps better track of node identities and structural properties. The pass-by-fragment semantics is subsequently refined to a pass-by projection semantics by means of a novel runtime XML projection technique, which safely eliminates most semantic differences between the local and remote execution of an XQuery expression, and strongly reduces message sizes. The proposed techniques are implemented in XRPC, a simple yet efficient XQuery extension that enables function-shipping by adding a remote procedure call mechanism to XQuery. Experiments on MonetDB/XQuery establish the performance potential of our XQuery decomposition techniques.

We demonstrate XRPC, a minimal XQuery extension that enables distributed querying between heterogeneous XQuery engines. The XRPC language extension enhances the existing concept of XQuery functions with the Remote Procedure Call (RPC) paradigm. XRPC is orthogonal to all XQuery features, including the XQuery Update Facility (XQUF). Note that executing xquf updating functions over XRPC leads to the phenomenon of distributed transactions. XRPC achieves heterogeneity by an open SOAP-based network protocol, that can be implemented by any engine, and an XRPC Wrapper that allows even XRPC-oblivious XQuery engines to handle XRPC requests efficiently. XRPC is fully implemented in the open-source MonetDB/XQuery engine, and is demonstrated here to co-operate with Saxon, Galax and X-Hive through the XRPC wrapper.