Global change refers to changes in the atmosphere, oceans, biosphere, pedosphere and lithosphere over the past century. It is a long process with large dimensions. Nowadays, satellite provides a regular platform to take measurements of all sensitivity factors of Earth. However, measurements taken from satellite platform is only available in a limited region on Earth with a short observing time. This is a new challenge for the purpose to observe large scale, long period Earth phenomena. In recent years, there has been a renewed interest in the development of robotic and manned exploration missions to the Moon. Here we propose the possibility of observing large-scale Earth phenomena from a lunar base. Atmospheric gravity waves, which is of interest to atmospheric research and important to the global change, are discussed as a sensitivity factor for lunar-based observation.

This paper presents ParTime, a parallel algorithm for temporal aggregation. Temporal aggregation is one of the most important, yet most complex temporal query operators. It has been extensively studied in the past, but so far there has only been one attempt to parallelize this operator. ParTime supports data parallelism and has a number of additional advantages: It supports the full bitemporal data model, it requires no a-priori indexing, it supports shared computation, and it runs well on modern hardware (i.e., NUMA machines with large main memories). We implemented ParTime in a parallel database system and carried out comprehensive performance experiments with a real workload from the airline industry and a synthetic benchmark, the TPC-BiH benchmark. The results show that ParTime significantly outperforms any other available temporal database system. Furthermore, the results show that ParTime is competitive as compared to the Timeline Index, the best known technique to process temporal queries from the research literature and which is based on pre-computation and indexing.

Following the adoption of basic temporal features in the SQL:2011 standard, there has been a tremendous interest within the database industry in supporting bi-temporal features, as a significant number of real-life workloads would greatly benefit from efficient temporal operations. However, current implementations of bi-temporal storage systems and operators are far from optimal. In this paper, we present the Bi-temporal Timeline Index, which supports a broad range of temporal operators and exploits the special properties of an in-memory column store database system. Comprehensive performance experiments with the TPC-BiH benchmark show that algorithms based on the Bi-temporal Timeline Index outperform significantly both existing commercial database systems and state-of-the-art data structures from research.

Modern data-centric flows in the telecommunications industry require real time analytical processing over a rapidly changing and large dataset. The traditional approach of separating OLTP and OLAP workloads cannot satisfy this requirement. Instead, a new class of integrated solutions for handling hybrid workloads is needed. This paper presents an industrial use case and a novel architecture that integrates key-value-based event processing and SQL-based analytical processing on the same distributed store while minimizing the total cost of ownership. Our approach combines several well-known techniques such as shared scans, delta processing, a PAX-fashioned storage layout, and an interleaving of scanning and delta merging in a completely new way. Performance experiments show that our system scales out linearly with the number of servers. For instance, our system sustains event streams of 100,000 events per second while simultaneously processing 100 ad-hoc analytical queries per second, using a cluster of 12 commodity servers. In doing so, our system meets all response time goals of our telecommunication customers; that is, 10 milliseconds per event and 100 milliseconds for an ad-hoc analytical query. Moreover, our system beats commercial competitors by a factor of 2.5 in analytical and two orders of magnitude in update performance.

Bi-temporal databases support system (transaction) and application time, enabling users to query the history as recorded today and as it was known in the past. In this paper, we study windows over both system and application time, i.e., bi-temporal windows. We propose a two-dimensional index that supports one-time and continuous queries over fixed and sliding bi-temporal windows, covering static and streaming data. We demonstrate the advantages of the proposed index compared to the state-of-the-art in terms of query performance, index update overhead and space footprint.

After more than a decade of a virtual standstill, the adoption of temporal data management features has recently picked up speed, driven by customer demand and the inclusion of temporal expressions into SQL:2011. Most of the big commercial DBMS now include support for bitemporal data and operators. In this paper, we perform a thorough analysis of these commercial temporal DBMS: We investigate their architecture, determine their performance and study the impact of performance tuning. This analysis utilizes our recent (TPCTC 2013) benchmark proposal, which includes a comprehensive temporal workload definition. The results of our analysis show that the support for temporal data is still in its infancy: All systems store their data in regular, statically partitioned tables and rely on standard indexes as well as query rewrites for their operations. As shown by our measurements, this causes considerable performance variations on slight workload variations and significant overhead even after extensive tuning.

Benchmarks are widely applied for the development and optimization of database systems. Standard benchmarks such as TPC-C and TPC-H provide a way of comparing the performance of different systems. In addition, micro benchmarks can be exploited to test a specific behavior of a system. Yet, despite all the benefits that can be derived from benchmark results, the effort of implementing and executing benchmarks remains prohibitive: Database systems need to be set up, a large number of artifacts such as data generators and queries need to be managed and complex, time-consuming operations have to be orchestrated. In this demo, we introduce a generic benchmarking service that combines a rich meta model, low marginal cost and ease of use, which drastically reduces the time and cost to define, adapt and run a benchmark.

Recent studies have shown that column stores can outperform row stores significantly. This paper explores alternative approaches to extend column stores with versioning, i.e., time travel queries and the maintenance of historic data. On the one hand, adding versioning can actually simplify the design of a column store because it provides a solution for the implementation of updates, traditionally a weak point in the design of column stores. On the other hand, implementing a versioned column store is challenging because it imposes a two dimensional clustering problem: should the data be clustered by row or by version? This paper devises the details of three memory layouts: clustering by row, clustering by version, and hybrid clustering. Performance experiments demonstrate that all three approaches outperform a (traditional) versioned row store. The efficiency of these three memory layouts depends on the query and update workload. Furthermore, the performance experiments analyze the time-space tradeoff that can be made in the implementation of versioned column stores.

Managing temporal data is becoming increasingly important for many applications. Several database systems already support the time dimension, but provide only few temporal operators, which also often exhibit poor performance characteristics. On the academic side, a large number of algorithms and data structures have been proposed, but they often address a subset of these temporal operators only. In this paper, we develop the Timeline Index as a novel, unified data structure that efficiently supports temporal operators such as temporal aggregation, time travel, and temporal joins. As the Timeline Index is independent of the physical order of the data, it provides flexibility in physical design; e.g., it supports any kind of compression scheme, which is crucial for main memory column stores. Our experiments show that the Timeline Index has predictable performance and beats state-of-the-art approaches significantly, sometimes by orders of magnitude.

Managing and accessing temporal data is of increasing importance in industry. So far, most companies model the time dimension on the application layer rather than pushing down the operators to the database, which leads to a significant performance overhead. The goal of this PhD thesis is to develop a native support of temporal features for SAP HANA, which is a commercial in-memory column store database system. We investigate different alternatives to store temporal data physically and analyze the trade-offs arising from different memory layouts which cluster the data either by time or by space dimension. Taking into account the underlying physical representation, different temporal operators such as temporal aggregation, time travel and temporal join have to be executed efficiently. We present a novel data structure called Timeline Index and algorithms based on this index, which have a very competitive performance for all temporal operators beating existing best-of-breed approaches by factors, sometimes even by orders of magnitude. The results of this thesis are currently being integrated into HANA, with the goal of being shipped to the customers as a productive release within the next few months.

In this demo, we present a prototype of a main memory database system which provides a wide range of temporal operators featuring predictable and interactive response times. Much of real-life data is temporal in nature, and there is an increasing application demand for temporal models and operations in databases. Nevertheless, SQL:2011 has only recently overcome a decade-long standstill on standardizing temporal features. As a result, few database systems provide any temporal support, and even those only have limited expressiveness and poor performance. Our prototype combines an in-memory column store and a novel, generic temporal index structure named Timeline Index. As we will show on a workload based on real customer use cases, it achieves predictable and interactive query performance for a wide range of temporal query types and data sizes.