Matrix Factorization Techniques for Recommender Systems

Research in closed-loop supply chains (CLSCs) has grown rapidly over the last 10 years. The authors offer a critical review of influential CLSC research that takes a business economics perspective. Much of the research was inspired by practice-driven thoughtpieces, and this helped to keep research focused on relevant issues. However, CLSC research has several assumptions, such as perfect substitution between new and remanufactured products that risk becoming institutionalized. There is a strong need to carefully examine current industrial practice so that research remains focused on relevant problems. Deeper understanding of consumer perceptions of remanufactured products, product diffusion, and valuation of returned products are needed for the field to continue to add insights into developing sustainable economies.

Production and Operations Management

Mit seiner Publikation “One Size Fits All: An Idea Whose Time Has Come and Gone?“ [St05] hat Michael Stonebraker 2005 die Datenbankszene provoziert. Er vertritt dort die These, dass die eingeführten und in vielen Kontexten eingesetzten „traditionellen“ Datenbanksysteme zwar für viele datenzentrische Anwendungen eingesetzt werden, aber keineswegs die optimalen Systeme für viele dieser Anwendungen (OLAP, Stream Processing, XML Datenbanken, wissenschaftliche Datenbanken, Textsuche) sind. In einer späteren Publikation [St07] stellte er sogar die These auf, dass es keine Anwendungen gibt, für die die traditionellen Datenbanksysteme die beste Alternative sind. Er sagte voraus, dass sich in der kommerziellen Welt eine Anzahl unabhängiger spezialisierter „neuer“ Datenbanksysteme aufsplitten werde, die bestenfalls durch ein gemeinsames Frontend verbunden sind.

"One Size Fits All": An Idea Whose Time Has Come and Gone?

Industrial as well as academic analytics systems are usually evaluated based on well-known standard benchmarks, such as TPC-H or TPC-DS. These benchmarks test various components of the DBMS including the join optimizer, the implementation of the join and aggregation operators, concurrency control and the scheduler. However, these benchmarks fall short of evaluating the "real" challenges imposed by modern BI systems, such as Tableau, that emit machine-generated query workloads. This paper reports a comprehensive study based on a set of more than 60k real-world BI data repositories together with their generated query workload. The machine-generated workload posed by BI tools differs from the "hand-crafted" benchmark queries in multiple ways: Structurally simple relational operator trees often come with extremely complex scalar expressions such that expression evaluation becomes the limiting factor. At the same time, we also encountered much more complex relational operator trees than covered by benchmarks. This long tail in both, operator tree and expression complexity, is not adequately represented in standard benchmarks. We contribute various statistics gathered from the large dataset, e.g., data type distributions, operator frequency, string length distribution and expression complexity. We hope our study gives an impetus to database researchers and benchmark designers alike to address the relevant problems in future projects and to enable better database support for data exploration systems which become more and more important in the Big Data era.

Get Real: How Benchmarks Fail to Represent the Real World

Indexes are essential for the efficient processing of database workloads. Proposed solutions for the relevant and challenging index selection problem range from metadata-based simple heuristics, over sophisticated multi-step algorithms, to approaches that yield optimal results. The main challenges are (i) to accurately determine the effect of an index on the workload cost while considering the interaction of indexes and (ii) a large number of possible combinations resulting from workloads containing many queries and massive schemata with possibly thousands of attributes.In this work, we describe and analyze eight index selection algorithms that are based on different concepts and compare them along different dimensions, such as solution quality, runtime, multi-column support, solution granularity, and complexity. In particular, we analyze the solutions of the algorithms for the challenging analytical Join Order, TPC-H, and TPC-DS benchmarks. Afterward, we assess strengths and weaknesses, infer insights for index selection in general and each approach individually, before we give recommendations on when to use which approach.

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Magic mirror in my hand, which is the best in the land?: an experimental evaluation of index selection algorithms

TPC-H continues to be the most widely used benchmark for relational OLAP systems. It poses a number of challenges, also known as "choke points", which database systems have to solve in order to achieve good benchmark results. Examples include joins across multiple tables, correlated subqueries, and correlations within the TPC-H data set. Knowing the impact of such optimizations helps in developing optimizers as well as in interpreting TPC-H results across database systems.This paper provides a systematic analysis of choke points and their optimizations. It complements previous work on TPC-H choke points by providing a quantitative discussion of their relevance. It focuses on eleven choke points where the optimizations are beneficial independently of the database system. Of these, the flattening of subqueries and the placement of predicates have the biggest impact. Three queries (Q2, Q17, and Q21) are strongly influenced by the choice of an efficient query plan; three others (Q1, Q13, and Q18) are less influenced by plan optimizations and more dependent on an efficient execution engine.

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Quantifying TPC-H choke points and their optimizations

Research in data management profits when the performance evaluation is based not only on individual components in isolation, but uses an actual DBMS end-to-end. Facilitating the integration and benchmarking of new concepts within a DBMS requires a simple setup process, well-documented code, and the possibility to execute both standard and custom benchmarks without tedious preparation. Fulfilling these requirements also makes it easy to reproduce the results later on. The relational open-source database Hyrise (VLDB, 2010) was presented to make the case for hybrid rowand column-format data storage. Since then, it has evolved from being a singlepurpose research DBMS towards becoming a platform for various projects, including research in the areas of indexing, data partitioning, and non-volatile memory. With a growing diversity of topics, we have found that the original code base grew to a point where new experimentation became unnecessarily difficult. Over the last two years, we have re-written Hyrise from scratch and built an extensible multi-purpose research DBMS that can serve as an easy-to-extend platform for a variety of experiments and prototyping in database research. In this paper, we discuss how our learnings from the previous version of Hyrise have influenced our re-write. We describe the new architecture of Hyrise and highlight the main components. Afterwards, we show how our extensible plugin architecture facilitates research on diverse DBMS-related aspects without compromising the architectural tidiness of the code. In a first performance evaluation, we show that the execution time of most TPC-H queries is competitive to that of other research databases.

Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management.

An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality.

Efficient Scalable Multi-attribute Index Selection Using Recursive Strategies

Most online markets are characterized by competitive settings and limited demand information. Due to the complexity of such markets, efficient pricing strategies are hard to derive. We analyze stochastic dynamic pricing models in competitive markets with multiple offer dimensions, such as price, quality, and rating. In a first step, we use a simulated test market to study how sales probabilities are affected by specific customer behaviors and the strategic interaction of price reaction strategies. Further, we show how different state-of-the-art learning techniques can be used to estimate sales probabilities from partially observable market data. In a second step, we use a dynamic programming model to compute an effective pricing strategy which circumvents the curse of dimensionality. We demonstrate that the strategy is applicable even if the number of competitors is large and their strategies are unknown. We show that our heuristic can be tuned to smoothly balance profitability and speed of sales. Further, our approach is currently applied by a large seller on Amazon for the sale of used books. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%.

Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach

Enterprise applications are traditionally divided in transactional and analytical processing. This separation was essential as growing data volume and more complex requests were no longer performing feasibly on conventional relational databases.

Martin Boissier

Papers

Quantifying TPC-H choke points and their optimizations

Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management.

Efficient Scalable Multi-attribute Index Selection Using Recursive Strategies

Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach

Main memory databases for enterprise applications