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Klauck, S., Schlosser, R.: Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming. ICDE, accepted for publication (short paper) (2019).
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Klauck, S., Schlosser, R.: A Comparison of Allocation Algorithms for Partially Replicated Databases. ICDE Demo, accepted for publication (2019).
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Dreseler, M., Kossmann, J., Boissier, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management. 22nd International Conference on Extending Database Technology (EDBT) (2019).
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Hiller, J., Kimmerlin, M., Plauth, M., Heikkila, S., Klauck, S., Lindfors, V., Eberhardt, F., Bursztynowski, D., Santos, J.L., Hohlfeld, O., Wehrle, K.: Giving Customers Control over Their Data: Integrating a Policy Language into the Cloud. 2018 IEEE International Conference on Cloud Engineering (IC2E) (2018).
Cloud computing offers the potential to store, manage, and process data in highly available, scalable, and elastic environments. Yet, these environments still provide very limited and inflexible means for customers to control their data. For example, customers can neither specify security of inter-cloud communication bearing the risk of information leakage, nor comply with laws requiring data to be kept in the originating jurisdiction, nor control sharing of data with third parties on a fine-granular basis. This lack of control can hinder cloud adoption for data that falls under regulations. In this paper, we show in six use cases how cloud environments can be enriched with policy language support to give customers control over cloud data. Our use cases are based on realizing policy language support in all three cloud environment layers, i.e., IaaS, PaaS, and SaaS. Specifically, we present policy-aware resource management (with OpenStack) and dynamic network configuration. With CERN's big data storage and the in-memory database Hyrise, we show realization for storage and further exemplify policy-aware cloud processing by network function virtualization which enables Orange to offload customer home gateways to the cloud. Finally, we discuss benefits of policy support in F-Secure's Security Cloud. These use cases show the feasibility of realizing customer control with policy support in the cloud. Thus, our work enables customers with regulated data to tap cloud benefits and significantly broadens the market for cloud providers.
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Klauck, S.: Scalability, Availability, and Elasticity through Database Replication in Hyrise-R. Proceedings of the 4th HPI Cloud Symposium “Operating the Cloud” 2016. pp. 1-10 (2017).
The growing analytical demand increases the importance of scalability and elasticity for mixed workload in-memory databases. Data replication is a way to cope with the growing demand and entails increased availability. In this paper, we describe different replication mechanisms, balancing query performance and availability. In addition, we outline how we implemented the cloud-ilities scalability, availability, and elasticity in Hyrise-R, a replication extension of the in-memory database Hyrise. Finally, we summarize further current research activities within the Hyrise project, i. e., data tiering, self-adaption and non-volatile RAM.
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Lindemann, J., Klauck, S., Schwalb, D.: A Scalable Query Dispatcher for Hyrise-R. Proceedings of the 3rd HPI Cloud Symposium “Operating the Cloud” 2015. pp. 25-32 (2016).
While single machines can handle the transactional database workload of most companies, the increasing analytical load will push them to their limit. For this reason, we extended the open source in-memory database Hyrise with the capability to form a database cluster for scalability and increased availability. This scale out and hot standby version is called Hyrise-R. It implements lazy master replication and has been shown to be well suited for mixed workloads as they exist in enterprise applications. In this paper we present our extension of Hyrise-R: a query dispatcher, which works fully transparently and implements an enhanced query distribution algorithm. The new distribution algorithm improves load balancing and prioritizes write requests for higher transaction throughput. In addition, we discuss our work in progress and planned activities for Hyrise-R.
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Klauck, S., Butzmann, L., Müller, S., Faust, M., Schwalb, D., Uflacker, M., Sinzig, W., Plattner, H.: Interactive, Flexible, and Generic What-If Analyses Using In-Memory Column Stores. Database Systems for Advanced Applications. pp. 488-497 (2015).
One well established method of measuring the success of companies are key performance indicators, whose inter-dependencies can be represented by mathematical models, such as value driver trees. While such models have commonly agreed semantics, they lack the right tool support for business simulations, because a flexible implementation that supports multi-dimensional and hierarchical structures on large data sets is complex and computationally challenging. However, in-memory column stores as the backbone of enterprise applications provide incredible performance that enables to calculate flexible simulation scenarios interactively even on large sets of enterprise data.
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Plattner, H., Mueller, S., Nica, A., Butzmann, L., Klauck, S.: Using Object-Awareness to Optimize Join Processing in the SAP HANA Aggregate Cache. Proceedings of the 18th International Conference on Extending Database Technology (EDBT), Brussels, Belgium (2015).
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Schwalb, D., Kossmann, J., Faust, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications. Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics (IMDM), in conjunction with VLDB 2015 Kohala Coast, Hawaii (2015).
In-memory database systems are well-suited for enterprise workloads, consisting of transactional and analytical queries. A growing number of users and an increasing demand for enterprise applications can saturate or even overload single- node database systems at peak times. Better performance can be achieved by improving a single machine’s hardware but it is often cheaper and more practicable to follow a scale-out approach and replicate data by using additional machines. In this paper we present Hyrise-R, a lazy master replication system for the in-memory database Hyrise. By setting up a snapshot-based Hyrise cluster, we increase both performance by distributing queries over multiple instances and availability by utilizing the redundancy of the cluster structure. This paper describes the architecture of Hyrise- R and details of the implemented replication mechanisms. We set up Hyrise-R on instances of Amazon’s Elastic Compute Cloud and present a detailed performance evaluation of our system, including a linear query throughput increase for enterprise workloads.
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Butzmann, L., Klauck, S., Mueller, S., Uflacker, M., Plattner, H., Sinzig, W.: Generic Business Simulation Using an In-Memory Column Store. Datenbanksysteme für Business, Technologie und Web (BTW), 16. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS) (2015).
Value driver trees are a well-known methodology to model dependencies such as the definition of key performance indicators. While the models have well-known semantics, they lack the right tool support for business simulations, because a flexible implementation that supports multidimensional, hierarchical value driver trees and data bindings is very complex and computationally challenging. This paper tackles this problem by proposing an approach for generic enterprise simulations which are based on value driver trees. Our approach is two-fold: we present the definition of a simulation meta model at design time, and the run-time simulation tool. The simulation meta model describes the structure of the dependency graph, the data binding, and the parametrization of the model to simulate data changes. The simulation tool can then be used to create and edit simulation model instances and run simulations in real-time by leveraging an in-memory column store. Besides the formal description of the approach, this work presents a prototypical implementation of the simulation tool and an evaluation using data of a consumer packaged goods company.
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Mueller, S., Butzmann, L., Klauck, S., Plattner, H.: An Adaptive Aggregate Maintenance Approach for Mixed Workloads in Columnar In-Memory Databases. Proceedings of the Thirty-Seventh Australasian Computer Science Conference (ACSC '14) - Volume 147. pp. 3-12 (2014).
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Mueller, S., Butzmann, L., Klauck, S., Plattner, H.: Materialized View Maintenance Leveraging In-Memory Data Structures. International Journal On Advances in Software, vol. 7, no. 3&4. (2014).
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Mueller, S., Butzmann, L., Klauck, S., Plattner, H.: Workload-Aware Aggregate Maintenance in Columnar In-Memory Databases. IEEE International Conference on Big Data (IEEE Big Data 2013), Silicon Valley, USA (2013).
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Mueller, S., Butzmann, L., Höwelmeyer, K., Klauck, S., Plattner, H.: Efficient View Maintenance for Enterprise Applications in Columnar In-Memory Databases. 17th IEEE International Enterprise Distributed Object Computing Conference (EDOC), Vancouver, Canada (2013).
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Zeier, A., Plattner, H., Butzmann, L., Klauck, S., Tinnefeld, C., Mueller, S.: Available-To-Promise on an In-Memory Column Store. Datenbanksysteme in Business, Technologie und Web (BTW 2011), 14. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), Proceedings, Kaiserslautern, Germany (2011).
Available-To-Promise (ATP) is an application in the context of Supply Chain Management (SCM) systems and provides a checking mechanism that calculates if the desired products of a customer order can be delivered on the requested date. Modern SCM systems store relevant data records as aggregated numbers which implies the disadvantages of maintaining redundant data as well as inflexibility in querying the data. Our approach omits aggregates by storing all individual data records in an in-memory, column-store and scans through all relevant records on-the-fly for each check. We contribute by describing the novel data organization and a lockingfree, highly-concurrent ATP checking algorithm. Additionally, we explain how new business functionality such as instant rescheduling of orders can be realized with our approach. All concepts are implemented within a prototype and benchmarked by using an anonymized SCM dataset of a Fortune 500 consumer products company. The paper closes with a discussion of the results and gives an outlook how this approach can help companies to find the right balance between low inventory costs and high order fulfillment rates.