Bringing Back-in-Time Debugging Down to the Database

With back-in-time debuggers, developers can explore what happened before observable failures by following infection chains back to their root causes. While there are several such debuggers for object-oriented programming languages, we do not know of any back-in-time capabilities at the database-level. Thus, if failures are caused by SQL scripts or stored procedures, developers have difficulties in understanding their unexpected behavior. In this paper, we present an approach for bringing back-in-time debugging down to the SAP HANA in-memory database. Our TARDISP debugger allows developers to step queries backwards and inspecting the database at previous and arbitrary points in time. With the help of a SQL extension, we can express queries covering a period of execution time within a debugging session and handle large amounts of data with low overhead on performance and memory. The entire approach has been evaluated within a development project at SAP and shows promising results with respect to the gathered developer feedback.Comment: 24th IEEE International Conference on Software Analysis, Evolution, and Reengineerin

Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case

Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data's full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.Comment: Accepted at International Conference on Database Systems for Advanced Applications (DASFAA 2019

How to Survive Dynamic Pricing Competition in E-commerce

ABSTRACT Pricing on e-commerce platforms is highly challenging. Sellers typically i) rival against dozens of competitors, ii) decide on prices for thousands of products, and iii) face steadily changing market situations. With respect to pricing, the challenge is to circumvent the curse of dimensionality to dynamically price products for a given market situation in a timely manner. In this project, we create a stochastic pricing model by analyzing recorded market data. This pricing model can be applied ad-hoc in less than a millisecond per item, allowing us to react immediately to new market situations. Our pricing approach is currently being applied in practice by a large German book seller on Amazon and outperforms the previous rule-based strategy by over 20% with respect to cash-in per book. CCS Concepts •Applied computing → Online shopping; E-commerce infrastructure; Decision analysis; Keywords Dynamic Pricing; Oligopoly Competition; Online Markets; Demand Estimation CHALLENGE Modern market platforms such as Amazon Marketplace or eBay are highly dynamic as sellers can observe the current market situation at any time and adjust their prices instantly. For sellers that handle large inventories, this dynamic is hard to manage as an optimal pricing decision requires handling a multitude of dimensions for each competitor (e.g., price, quality, shipping time, shipping costs, rating). Moreover, financial aspects such as discounting as well as inventory holding costs have to be taken into account. In this project, we partner with adanbo GmbH. adanbo is among the top 10 sellers for used books on Amazon in Germany with an inventory of over 80,000 distinct books (ISBN), each with multiple items (1-20). Our seller can decide -to some extent -on the replenishment of used books (by choosing purchase prices). However, supply is limited and it is not possible to directly reorder specific books. Hence, the challenge is to extract as much profit as pos- Copyright is held by the author(s). sible from a given number of books (inventory level) in a reasonable amount of time. The pricing strategy of our project partner is characterized by a rule-based system that has been developed over the past years by carefully adjusting rules to lessons learned from selling books on Amazon. As our project partner has more than 10 years of experience in the market, we consider his strategy to be effective and accurate. However, market dynamics are increasingly sophisticated making rule-based strategies increasingly hard to handle and maintain. Our goal is to develop a pricing strategy that maximizes expected discounted long-term profits while taking into account the constraints mentioned above. We seek to compute data-driven pricing strategies that are applicable even for large inventories. DATA-DRIVEN PRICING MODEL The project is devoted to revenue managemen