In recent years, Supply Chain Management (SCM) has become one of the most widely discussed issues on top management`s agenda. One essential precondition for the implementation of SCM concepts are powerful information systems. Many manufacturing companies deploy the logistics modules of the SAP R/3 system. In 1997 SAP enlarged its focus beyond Enterprise Resource Planning (ERP) systems and started an SCM initiative, which resulted in the Advanced Planner and Optimizer (APO) system. Today, APO has become one of the main components of the mySAP.com solutions. This book describes the principles and methods of intra- and inter-company SCM with reference to Release 4.6 of the SAP R/3 and to Release 3.0 of the SAP APO system. It also discusses potential future developments of these widely used software systems. The final chapter of the book contains an annotated overview of the most valuable online resources dealing with SCM.

Generative artificial intelligence (GenAI) is one of the most promising recent advances in digital technology. However, research often focuses on specific application scenarios, case studies and experiments. Overarching and comprehensive studies that consider potentials and challenges for the entire field of GenAI across domains are rather scarce. In this paper, the four domains of text, audio, image and code generation are examined by means of a systematic literature review. Opportunities for industry and society are discussed, with the aim of providing a conceptual model that enables a quick assessment of the current state-of-the-art and identifies applications for GenAI that are either not yet sufficiently researched and therefore invite further exploratory investigations, or are well researched and therefore represent recognized yet less experimental fields.

AI—including generative AI—is radically transforming business. Are you ready? Accenture technology leaders Paul Daugherty and Jim Wilson provide crucial insights and advice to help you meet the challenge.

We explore the new approaches to intelligence, data, expertise, architecture, and strategy (IDEAS) that are redefining innovation. Taken together, these turns toward the radically human in technology will remake business.

In the Mindset Design Lab kitchen (sometimes known as Gopher Works), we strive tirelessly with our SAP customers to create the perfect business solution recipe. Some of our best dishes have the rich, hearty flavor of S/4HANA, others have a creamy Cloud Platform sauce, and still others take on an airy sweetness with machine learning and AI. Our customers take these dishes back to their business, and voila! Instant satisfaction.

We recently submitted our product CloudSimple to the Chrome web store for Google Sheets add-ons. Am I tired? Yes. Did I spend a few too many late nights in front of a code editor? Yes. Would I do it again? One hundred bajillion percent yes. So, having spent the last few month with my head way inside the process to integrate Google docs with an SAP backend through NetWeaver Gateway, I humbly offer a few tips to the blogosphere for anyone wanting to do the same thing.

Fearless Founder Gavin recently finished a Herculean task: setting up SAP Fiori on our AWS development landscape. I would compare this feat to casting the One Ring into the fires of Mount Doom, but Gavin’s really not all that hobbit-like, and he only encountered metaphorical orcs on his journey.

I’ll gladly admit: I was really excited when the announcement came last week that Google Docs and Sheets would be open to third-party add-ons developed in Google Apps Script. It opens the door to all sorts of interesting application integration capabilities, and enhances them with the collaboration tools and ubiquity of Google Drive.

So – you’ve decided to expose some SAP Business Suite data to outside applications through SAP NetWeaver Gateway. You’ve seen the tutorials, you’ve read up on how to build services based on RFCs or entirely in custom code, and if you’re really ambitious you’ve even powered your way through the excellent OData and SAP NetWeaver Gateway book from SAP Press.

A number of governments and organizations around the world agree that the first step to address national and international problems such as energy independence, global warming or emergency resilience, is the redesign of electricity networks, known as Smart Grids. Typically, power grids have broadcast power from generation plants to large population of consumers on a sub-optimal way. Nevertheless, the fusion of energy delivery networks and digital information networks, along with the introduction of intelligent monitoring systems (Smart Meters) and renewable energies, would enable two-way electricity trading relationships between electricity suppliers and electricity consumers. The availability of real-time information on electricity demand and pricing, would enable suppliers optimizing their delivery systems, while consumers would have the means to minimize their bill by turning on appliances at off-peak hours. The construction of the Smart Grid entails the design and deployment of information networks and systems of unprecedented requirements on storage, real-time event processing and availability. In this paper, a series of system architectures to store and process Smart Meter reading data are explored and compared aiming to establish a solid foundation in which future intelligent systems could be supported.

The retailing industry witnessed a significant shift in the past years, which introduced significant modifications in the standard procedures and regular practices of supply chains (SC). These necessary modifications and worldwide distress caused major disruptions and instabilities in SC. Organizations started developing digital-transformation strategies, which include the integration and analysis of external data sources to detect disruptions in SC to retain a competitive advantage in the market. Developing such strategies requires exploring current technologies and investigating the use of cloud capabilities for adaptive supply chain management (A-SCM). The lack of flexibility in strategic and operative supply chain practices causes a significant loss of optimization potential. Therefore, in this study, we investigate data sources and solution techniques that might be beneficial to developing A-SCM practices. Based on the conducted literature analysis and cloud technologies, we present a hybrid architecture for A-SCM, integrating Google Cloud technologies, SAP HANA, simulation, and optimization components.

In this research article, the buzzwords of digital thread, digital twin, and Industry 4.0 are examined by means of a systematic literature review. The key concepts shaping these paradigms are investigated to achieve an overview of existing solutions. First, the body of the literature is explored to provide general observations on similarities and differences between the concepts. Subsequently, an overview of key technologies is provided that are necessary to achieve the vision of the digital thread. Based on the identified technologies, state-of-the-art solutions relating to digital thread are discussed. Finally, this research work presents a high-level architecture for realizing the digital thread based on the conducted technology analysis. The architecture is also dedicated to providing a cloud-based observation combining business solutions (e.g., SAP products) and cloud platforms (e.g., Google Cloud). To conclude the research carried out here, an outlook is given on possible future developments based on the work accomplished

After 50 years of successful development, business IT has reached a new turning point. Here, the authors demonstrate for the first time how in-memory computing will transform business processes in the future. Until now, corporate data has been distributed across different databases for performance reasons: Analytical data resides in data warehouses and is regularly synchronized using transactional systems. This fragmentation makes flexible, real-time reporting of current data impossible. But thanks to powerful multi-core CPUs, large main memory, cloud computing, and ever-improving mobile devices, companies are increasingly leaving this restrictive model behind. The authors present technologies that enable analytical and transactional processing in real time, thus paving new ways for business.

In general, methods and apparatus, including computer program products, implementing and using techniques for providing a discovery service in a unique identifier network are described. Said discovery service is suitable for tracking and tracing a query item represented by a unique identifier in a unique identifier network. In particular, a search method for a containment-aware discovery service is described.

In general, methods and apparatus, including computer program products, implementing and using techniques for providing a discovery service in a unique identifier network are described. Said discovery service is suitable for tracking and tracing a query item represented by a unique identifier in a unique identifier network. In particular, a communication protocol for a containment-aware discovery service is described.

In general, methods and apparatus, including computer program products, implementing and using techniques for providing a discovery service in a unique identifier network are described. Said discovery service is suitable for tracking and tracing a query item represented by a unique identifier in a unique identifier network. In particular, a filter method for a containment-aware discovery service is described.

The invention relates to a computer-implemented method for operating a database comprising the step of updating a binary status of a tuple in the database. The binary status field either contains a null value or a time stamp. The update can be done in-place or by using an insert-only approach.

This chapter describes why we believe the technology used in SanssouciDB is a good fit for Cloud Computing. We begin by defining Cloud Computing and then go on to describe the different types of applications that are suited for the cloud. Section 8.3 takes a provider’s point of view and describes the type of infrastructure required to offer cloud services. We also examine multi-tenancy, replication, choice of hardware, and energy efficiency. Finally, we offer our conclusions regarding how in-memory computing will play an ever-increasing role in Cloud Computing.

Providing enterprise users with information they require, when they require it is not just a question of using the latest technology to store information in an efficient manner. Enterprise application developers and users need ways of accessing and storing their information that are suited to the tasks they wish to carry out. This includes things like making sure the most relevant data is always stored close to the CPU, while data that is no longer required for the day-to-day running of the business is stored in slower, cheaper storage; allowing new columns to be added to tables if customers need to customize an application, and allowing developers to choose the most efficient storage strategy for their particular task. The work of an application developer can also be made easier if they are able to read and write data in a way that fits in with the business process they are modeling. Finally, users and developers also need to have confidence that their database will be available when they need it, and that they will not lose their data if the power goes out or a hardware component fails. In this chapter we describe how data is accessed and organized in SanssouciDB, to meet the requirements we have outlined.

This chapter provides an overview of how we realize our vision of providing real-time data about a business available in real time. We show how using an IMDB, designed specifically for enterprise applications, provides significant performance advantages. We describe the particular technologies we have used and why these are the best choices for our particular purposes. Throughout the chapter we show how best to utilize the latest hardware to provide maximum performance and also how we organize data to efficiently use the large main memory of the servers on which the database runs.

In this chapter, we offer our most important insights regarding operational and analytical systems from a business perspective. We describe how they can be unified to create a fast combined system. We also discuss how benchmarking can be changed to evaluate the unified system from both an operational and analytical processing perspective. As we saw earlier, it used to be possible to perform Business Intelligence (BI) tasks on a company’s operational data. By the end of the 90s, however, this was no longer possible as data volumes had increased to such an extent that executing long-running analytical queries slowed systems down so much that they became unusable. BI solutions thus evolved over the years from the initial operational systems through to the current separation into operational and analytical domains. As we have seen, this separation causes a number of problems. Benchmarking systems have followed a similar trajectory, with current benchmarks measuring either operational or analytical performance, making it difficult to ascertain a systems true performance. With IMDB technology we now have the opportunity to once again reunite the operational and analytical domains. We can create BI solutions that analyze a company’s up-to-the-minute data without the need to create expensive secondary analytical systems. We are also able to create unified benchmarks that give us a much more accurate view of the performance of the entire system. This chapter describes these two topics in detail. In Sect. 7. 1, we cover the evolution of BI solutions from the initial operational systems through the separation into two domains, and then we give a recommendation regarding the unification of analytical and operational systems based on in-memory technology. In Sect. 7. 2, we examine benchmarking across the operational and analytical domains.

In this chapter we describe the detailed physical and logical foundations of SanssouciDB. Simply running an existing database on a machine with a lot of main memory and a large number of cores will not achieve the speed-up we are seeking. To achieve our goal of giving business users information at their fingertips, we need to create a database that fully leverages these technological advantages. Chief among these are the efficient use of main memory, and the parallelization of tasks to take advantage of all the available cores. In addition to these physical considerations, we also use logical design to increase performance. We lay out data in a way that suits the operations being carried out on it and also minimize the amount of data that needs to be processed.

This final chapter demonstrates that our vision of an in-memory database for large-scale enterprise applications is already in the process of becoming reality. The first product that implements many of the concepts of SanssouciDB is the new in-memory data management solution released by SAP at the end 2010. Companies can begin using in-memory applications today and benefit from a massive performance increase. We show how this transition can occur seamlessly and without disruption. The chapter closes with the first customer proof points by presenting application examples.

Today, even small businesses operate in different geographical locations and service different industries. This can create a number of challenges including those related to language, currencies, different regulatory requirements, and diverse industry expectations. For large organizations with a wider reach, the challenges are even greater. As organizations grow, they also need to keep track of huge amounts of information across different business areas. Modern enterprise applications need to be able to cope with these demands in a timely manner. In this chapter we provide a brief introduction to modern enterprise applications, describing selected tasks they perform and the business areas that they cover.

Sub-second respond time and real-time analytics are key requirements for applications that allow natural human computer interactions. We envision users of enterprise applications to interact with their software tools in such a natural way, just like any Internet user interacts with a web search engine today by refining search results on the fly when the initial results are not satisfying. In this initial chapter, we illustrate this vision of providing business data in real time and discuss it in terms of desirability, feasibility, and viability. We first explain the desire of supplying information in real time and review sub-second response time in the context of enterprise applications. We then discuss the feasibility based on in-memory databases that leverage modern computer hardware and conclude by demonstrating the economic viability of in-memory data management.

A Computer system is a sophisticated combi-nation of software and hardware components that work closely together to solve today's problems. While algorithms within a software component itself can be implemented and optimized by a small group of developers, algorithms for an entire software stack are more complex and their development gets expensive. To speed up the development of in-memory database specific scheduling algorithms for oper-ating systems, we implemented a simulation model based on SAP's TREX in-memory column database. This simulation is used to optimize query scheduling and execution algorithms without the necessity of modifying every component within the existing system. To quantify the advantages and draw-backs of the different scheduling algorithms, we use the Star Schema Benchmark to measure the cost reduction per user with regards to the implemented operating system scheduling techniques.

The historically introduced separation of online analytical processing (OLAP) from online transaction processing (OLTP) is in question considering the current developments of databases. Column-oriented databases mainly used in the OLAP environment so far, with the addition of in-memory data storage are adapted to accommodate OLTP as well, thus paving the way for mixed OLTP and OLAP process-ing. To assess mixed workload systems benchmarking has to evolve along with the database technology. Especially in mixed workload scenarios the question arises of how to layout the database. In this paper, we present a case study on the impact of database design focusing on normalization with respect to various workload mixes and database implementations. We use a novel benchmark methodology that provides mixed OLTP and OLAP workloads based on a real scenario.

Fast execution of analytical and transactional queries in column-oriented in-memory DBMS is achieved by combining a read-optimized data store with a write-optimized differential store. To main-tain high read performance, both structures must be merged from time to time. In this paper we describe a new merge algorithm that applies full and partial merge operations based on their costs and improvement of read performance. We show by simulation that our algorithm reduces merge costs significantly for workloads found in enterprise applications, while improving read performance at the same time.

In the last fifty years the world has been completely transformed through the use of IT. We have now reached a new inflection point. This book presents, for the first time, how in-memory data management is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. This book provides the technical foundation for processing combined transactional and analytical operations in the same database. In the year since we published the first edition of this book, the performance gains enabled by the use of in-memory technology in enterprise applications has truly marked an inflection point in the market. The new content in this second edition focuses on the development of these in-memory enterprise applications, showing how they leverage the capabilities of in-memory technology. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes.

The pharmaceutical industry suffers from steadily increasing counterfeit rates. RFID technology is named as a foundation for an integer and counterfeit-resistant pharmaceutical supply chain. A transformation toward an RFID-aided supply chain involves both technical and business adaptions. We share technical insights of our proposed architecture for storing and searching pharmaceutical RFID event data. From the business perspective, we discuss the viability of RFID-based anti-counterfeiting with respect to its monetary impacts. We define qualitative requirements for software components developed for RFID and provide a quantitative analysis of operational costs for a dedicated service provider for anti-counterfeiting. Its implementation and amortization is discussed for different operating setups to enable access to RFID technology for all participants of pharmaceutical the supply chain

In-memory databases are developed to keep the entire data in main memory. Compared to traditional database sys- tems, read access is now much faster since no I/O access to a hard drive is required. In terms of write access, mechanisms are available which provide data persistence and thus secure transactions.

In-memory databases keep the entire data set permanently in main memory. This allows read access to be much faster than with traditional database systems, as no I/O access to the hard disk is required.

How do designers leverage information and communication technology to collaborate with team partners and other process participants? Given the increasingly complex, distributed, and virtual setups of design environments and processes, answering this question is challenging. At HPI, we have developed computational data collection and analysis techniques to improve the efficiency and range of observations in technology-enabled design spaces. Using our software, we were able to capture and evaluate complex characteristics of online interactions in distributed design teams at quasi real-time. Besides new insights into the communication behavior of design teams, it could be demonstrated that communication activity signatures of high-performance design teams are significantly different than those of low-performance teams. The combination of new techniques along with quantifiable performance metrics provides a stable foundation for real-time design team diagnostics.

Cloud computing’s usage-based pricing model creates an incentive for subscribers to optimize the utilization of the rented resources. The goal of the current work is to devise a formal approach for distributing workload among a minimum number of servers. The paper models this problem as a set partitioning problem and describes two solution approaches. The first one generates a set of candidate blocks and then composes an optimal partition by solving an integer programming problem. The second approach solves the set partitioning problem with column generation technique. Both methods were implemented and evaluated. The paper reports the corresponding experiment results.

Location-based event data is captured in RFID-aided supply chains for tacking individual goods. They are stored in distributed event repositories by involved supply chain parties. Performing anti-counterfeiting checks involves exchange of event data without exposure of sensitive business secrets. Current EPC global standards leave the definition of security strategies open for concrete implementation. We consider data security as the major aspect that needs to be clarified before wide adaption of EPC global standards will be considered by industries. The given work contributes by defining security extensions for EPC global networks and sharing implementation details of our research prototype. We show that incorporating in-memory technology enables history-based access control while keeping response times fast.

Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process introduces significant overheads and unacceptable downtimes in update intensive systems, aspiring to combine transactional and analytical workloads into one system. In the first part of the paper, we report data analyses of 12 SAP Business Suite customer systems. In the second half, we present an optimized merge process reducing the merge overhead of current systems by a factor of 30. Our linear-time merge algorithm exploits the underlying high compute and bandwidth resources of modern multi-core CPUs with architecture-aware optimizations and efficient parallelization. This enables compressed in-memory column stores to handle the transactional update rate required by enterprise applications, while keeping properties of read-optimized databases for analytic-style queries.

Prior applications of a system to monitor IT-mediated communication activities of design teams provided new insights into the collaboration behavior during the early phases of concept creation and prototyping. We now take our approach to the next level by sketching an architecture for a platform that aims to establish ‘out- of-the-box’ monitoring capabilities for virtual team environments and to facilitate the sharing and evaluation of recorded activities within a larger research community. To further demonstrate the flexibility and applicability of our instrument, we present results and experiences gained from a recently conducted observation of software engineering teams. Our vision is a common service for capturing and analyzing virtual collaboration activities that promotes comparative research and team diagnostics in engineering design.

The importance of reporting is ever increasing in today’s fast-paced market environments and the availability of up-to-date information for reporting has become indispensable. Current reporting systems are separated from the online transaction processing systems (OLTP) with periodic updates pushed in. A pre-defined and aggregated subset of the OLTP data, however, does not provide the flexibility, detail, and timeliness needed for today’s operational reporting. As technology advances, this separation has to be re-evaluated and means to study and evaluate new trends in data storage management have to be provided. This article proposes a benchmark for combined OLTP and operational reporting, providing means to evaluate the performance of enterprise data management systems for mixed workloads of OLTP and operational reporting queries. Such systems offer up-to-date information and the flexibility of the entire data set for reporting. We describe how the benchmark provokes the conflicts that are the reason for separating the two workloads on different systems. In this article, we introduce the concepts, logical data schema, transactions and queries of the benchmark, which are entirely based on the original data sets and real workloads of existing, globally operating enterprises.

Key-value stores which keep the data entirely in main memory can serve applications whose performance criteria cannot be met by disk-based key-value stores. This paper evaluates the performance implications of cache-conscious data placement in an in-memory key-value store by examining how many values have to be stored consecutively in blocks in order to fully exploit memory locality during bandwidth-bound operations. We contribute by introducing a random block traversal main memory access pattern, by describing the corresponding memory access costs as well as by formally and experimentally deriving the correlation between block size and throughput. Our calculations and experiments vary the value and block sizes as well as their placement in the memory and derive their impact on cache-misses throughout the different memory hierarchies, the ability to prefetch data, and the number of needed CPU cycles to perform a certain set of data operations. The paper closes with the insight that a block-wise grouping of relatively few key-value pairs increases the throughput up to a factor six and with a discussion which implications a block-wise grouping of data has on the system design key-value store.

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. While many research activities in recent years focussed on the optimization of such separation - particularly in the last decade - databases as well as hardware continued to develop. On the one hand there are data management systems that organize data column-oriented and thereby ideally fulfill the requirement profile of analytical requests. On the other hand significantly more main memory is available to applications that allow to store the complete compressed database of an enterprise in combination with the equally significantly enhanced performance. Both developments enable processing of complex analytical requests in a fraction of a second and thus facilitate complete new business processes and -applications. Obviously the question arises whether the artificially introduced separation between OLTP and OLAP can be revoked and all requests be handled on a combined data set. This paper focuses on the characteristics of data processing in enterprise applications and demonstrates how selected technologies can optimize data processing. A further trend is the use of cloud computing and with it the outsourcing of the data centre to optimize cost efficiency. Here column-oriented inmemory databases are helpful as well as they permit a greater throughput, which in turn enables more effective use of the hardware and thus saves costs.

Enabling fast and detailed insights over large portions of source code is an important task in a global development ecosystem. Numerous data structures have been developed to store source code and to support various structural queries, to help in navigation, evaluation and analysis. Many of these data structures work with tree-based or graph-based representations of source code. The goal of this project is to elaborate a data storage that enables efficient storing and fast querying of structural information. The naive adjacency list method has been enhanced with the use of recent data compression approaches for column-oriented databases to allow no-loss albeit compact storage of fine-grained structural data. The graph indexing has enabled the proposed data model to expeditiously answer fine-grained structural queries. This paper describes the basics of the proposed approach and illustrates its technical feasibility.

Integrating RFID technology In current supply chains involves the development and testing of various IT systems, such as event repositories. Their development requires excessive real-world event data to verify architecture designs, access control, response time behavior, etc. Due to lack of industry-wide adoption of RFID technology, real-world event data for global implementations is not available, today. In this work, we present a configurable simulator for RFID-aided supply chains that is capable to create consistent and realistic event data. We used our generated event data as the basis for getting real-world event data to define requirements, develop, and test RFID systems with concrete industry-specific event data before the industrial adoption of RFID technology succeeded. As a result, we were able to verify IT components in EPCglobal networks with real-world data long before concrete industry data is available.

Source code search is an important tool used by software engineers. However, until now relatively little is known about what developers search for in source code and why. This paper addresses this knowledge gap. We present the results of a log file analysis of a source code search engine. The data from the log file was analyzed together with the change history of four development and maintenance systems. The results show that most of the search targets were not changed after being downloaded, thus we concluded that the developers conducted searches to find reusable components, to obtain coding examples or to perform impact analysis. In contrast, maintainers often change the code they have downloaded. Moreover, we automatically categorized the search queries. The most popular categories were: method name, structural pattern, and keyword. The major search target was a statement. Although the selected data set was small, the deviations between the systems were negligible, therefore we conclude that our results are valid.

While analyzing a log file of a text-based source code search engine we discovered that developers search for fine-grained syntactical patterns in 36% of queries. Currently, to cope with queries of this kind developers need to use regular expressions, to add redundant terms to the query or to combine searching with other tools provided by the development environment. To improve the expressiveness of the queries, these can be formulated as tree patterns of abstract syntax trees. These search patterns can be expressed by using query languages, such as XPath. However, developers usually do not work with either XPath or with AST. To shield developers from the complexity of query formulation we propose using sample code snippets as queries. The novelty of our approach is the combination of a query language that is very close to the surface programming language and a special database technology to store a large amount of abstract syntax trees. The advantage of this approach over existing source code query languages and search engines is the performance of both query formulation and query execution. This paper describes the technical details of the method and illustrates the value of this approach with performance measures and an industrial controlled experiment. All developers were able to complete the tasks of the experiment faster and more accurately by using our tool (ACS) than by using a text-based search engine. The number of false positives in the result lists was significantly decreased.

In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the cluster will lead to violations of response time goals. This model is then extended to capture drops in capacity incurred by migrating tenants between servers. We present an algorithm for moving tenants around the cluster to ensure that response time goals are met. In so doing, the number of servers in the cluster may be dynamically increased or decreased. The model is also extended to manage multiple copies of a tenant's data for scalability and availability. We validated the model with an implementation of a multi-tenant clustering framework for SAP's in-memory column database TREX.

Current database benchmarks are either focusing on online transaction processing (OLTP) or on online analytical pro- cessing (OLAP) systems. This traditional separation has to be reevaluated to reflect current trends in the design of database systems. We see a need for a realistic benchmark workload taking both aspects into account. Therefore, we defined a mixed workload and illustrate ways to apply our workload to evaluate the influence of database design on system performance.

The early stages of engineering projects are considered the most critical phase of a product lifecycle and need to be better understood. The augmented virtualization and geographic dispersion of project environments create demand for an adaptive design research methodology, which takes the increasing role of distributed online interactions into account. This work presents a generic approach for the quasi-real-time exploration of collaboration structures captured from heterogeneous groupware and communication resources. We introduce Team Collaboration Networks (TCN) as a model to describe the temporal relationships between different actors and information resources over the course of collaboration. A service-based TCN implementation has been applied in eleven distributed engineering design projects to support in the assessment of communication patterns and to provide a live view into the online communication activities of conceptual design teams. The key findings of this pilot application are presented.

Traditionally, enterprise data management is divided into separate systems. Online Transaction Processing (OLTP) systems are focused on the day to day business by being optimized for retrieving and modifying complete entities. Online Analytical Processing (OLAP) systems initiate queries on specific attributes as these applications are optimized to support decision making based on the information gathered from many instances. In parallel both hardware and database applications are subject to steady improvements. For example, today's size of main memory in combination with the column oriented organization of data offer completely new possibilities such as real time analytical ad hoc queries on transactional data. Especially latest development in the area of main memory database systems raises the question whether those databases are capable of handling both OLAP and OLTP workloads in one system. This Paper discusses requirements for main memory database systems managing both workloads and analyses using appropriate data structures.

Assuming the feasibility of main-memory database management systems, the current research aims at designing a new type of data manipulation API, called Business Object Query Language (BOQL), specifically tailored for in-database data manipulation in main-memory ERP systems. The paper contributes the concept of business object virtualization and describes a query processor that takes advantage of this concept. The first serves as a means of grouping raw memory-resident data into high-level data structures, while the second exposes a flexible query-like API to manipulate the high-level data structures. Special effort has been dedicated to integrating the API into C++ programming language.

A lot of tree-shaped data exists: XML documents, abstract syntax trees, hierarchies, etc. To accelerate query processing on trees stored in a relational database a pre-post-ordering can be used. It works well for locating ancestors of a single or few vertices because pre-post-ordering avoids recursive table access. However, it is slow if it comes to locating ancestors of hundreds or thousands of vertices because ancestors of each of the input vertices are located sequentially. In this paper, two novel algorithms (sort-tilt-scan and single-pass-scan) for solving this problem are proposed and compared with a naïve approach. While the sort-tilt-scan improves the performance by a constant factor, the single-pass-scan achieves a better complexity class. The performance gain is achieved because of a single table scan which can locate all result vertices by a single run. Using generated data, this paper demonstrates that the single-pass-scan is orders of magnitude faster than the naïve approach.

Agile software development processes are widely adopted in software engineering projects. Their low organizational overhead and iterative nature make them ideal choices for small development teams. The application of those methods in software projects that require collaboration between multiple sub-teams is a challenging task that remains subject to intensive research. Especially the initial phases of such projects are crucial for project success since a problem-free inception period generates a basis for efficient development later in the process. We introduce a testbed that allows analyzing collaboration processes during those early stages of software development within a low-risk, educational setup. Participants of a software engineering lecture form development teams of considerable size and develop real-life applications in a realistic, yet controlled, environment. By combining manual observations with the computational analysis of digital collaboration artifacts we are able to gain insights into distinctive patterns of collaboration activity and reason about their triggers within the process setup.

The use of RFID technology for anti-counterfeiting involves complex event processing, which exceeds the current state of the art of product tracking and tracing implementations. The use of RFID requires storing good related events for anti-counterfeiting in a distributed way by each handling party individually. We define a formal approach for modeling of data entities, events, and operations that correlate to handling of pharmaceutical goods. This supports the operation of a unified service provider for anti-counterfeiting that matches virtual product history and event data from distributed repositories of involved supply chain roles. Our contribution includes a quantitative analysis of the expected data amount and related costs for operating this service provider by applying our formal approach to a concrete pharmaceutical supply chain.

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.

For traditional data warehouses, mostly large and expensive server and storage systems are used. For small- and medium size companies, it is often too expensive to implement and run such systems. Given this situation, the SaaS model comes in handy, since these companies might opt to run their OLAP as a service. The challenge is then for the analytics service provider to minimize TCO by consolidating as many tenants onto as few servers as possible, a technique often referred to as multi-tenancy. In this article, we report on three different results on our research around building a cluster of multi-tenant main memory column databases for analytics as a service. For this purpose we ported SAP’s in-memory column database TREX to run in the Amazon cloud. We evaluated the relation between data size of a tenant and number of queries per second and created a formula which allows us to estimate how many tenants with different sizes and request rates can be put on one instance for our main memory database. We discuss findings on cost/performance tradeoffs between reliably storing the data of a tenant on a single node using a highly-available network attached storage, such as Amazon EBS, vs. replication of tenant data to a secondary node where the data resides on less resilient storage. We also describe a mechanism to provide support for historical queries across older snapshots of tenant data which is lazy-loaded from Amazon’s S3 near-line archiving storage and cached on the local VM disks.

To meet the performance requirements of enterprise application for both, transactional application as well as analyt-ical scenarios, data storage of in-memory databases are split into two parts: One optimized for reading and a write-optimized differential buffer. The read-optimized main storage together with the differential buffer for inserts provide the current state of the database. In regular intervals the differential buffer is merged with the main database to maintain compression and query performance. This merge process runs asynchronous to minimize the impact on query performance. However, simple duplication of the data structures prior to the merge process lead to a main memory consumption of at least twice the size of the database. In this paper we propose a differential merge update based on single columns. In typical enterprise application data environments this leads to a significant reduction of memory consumption as this type of applications tend to store transactional data in very large single tables. The Single Column Merge has been implemented in HYRISE and proved in a test scenario based on real enterprise data.

The way teams use virtual collaboration tools, such as wikis, email systems, social networks, or version control systems can provide indicators for the success or failure of projects. We previously created a platform that allows to collect and analyze these virtual collaboration activities during project runtime in a non-interfering manner. In this paper, we provide a formal definition of collaboration patterns to enable sharing of beneficial or detrimental collaboration behavior amongst scientist and practitioners. We further define a mapping from collaboration pattern descriptions to SPARQL queries that allows to automatically test other projects for occurrences of the described behavior. By that, we provide a research tool that is able to stimulate relevant and rigorous findings in empirical engineering research and lead to the creation of a shared repository of patterns that reflect best practices in virtual team collaboration.

— Prior applications of a system to monitor ITmediated communication activities of engineering teams provided new insights into the collaboration behavior during the early phases of engineering projects. Building on those findings, we now present an architecture for a platform that aims to establish ‘out-of-the-box ’ monitoring capabilities for virtual team environments and to facilitate the sharing and evaluation of recorded activities within a larger research community. To demonstrate the applicability of our instrument, we present the intended usage workflow and provide an outline of current applications in the domains of software engineering and engineering design. Our vision is a common service for capturing and analyzing virtual collaboration activities that promotes comparative research and team diagnostics across engineering disciplines.

In RFID-aided supply chains captured location-based event data is stored in distributed repositories. Performing anti-counterfeiting involves checks on the good's path in the supply chain. The path is reconstructed by querying corresponding event data from distributed repositories. The object name service performs lookups of relevant event repositories in EPCglobal networks. Attacking the lookup process can be used to break privacy of inquirers, e.g. to derive product and user profiles. We share details about our security enhancement prototypes to protect the privacy of querying parties. Our developed enhancements are designed for easy integration into existing network infrastructures without major efforts.

Anti-counterfeiting in RFID-aided supply chains builds on the retrieval and analysis of distributed EPC event data. Combining event data with third-party information can lead to exposure of business secrets or product resp. user profiles. We share details about our license-based access control prototype to protect the privacy of business parties. Our developed security enhancements are designed following the principle privacy-by-design proposed by the European Commission to minimizing the amount of exposed data for unknown supply chain parties.

Supply chain visibility and real-time awareness are two of the major drivers for the implementation of Auto-ID technologies in Supply Chain Management. A prerequisite for achieving real-time awareness and company overlapping visibility is an infrastructure to enable companies to share supply chain information in a reliable and secure way. EPCglobal proposes an application layer protocol called the EPC discovery service, which is supposed to provide services to gather supply chain information from a number of independent resources, across company borders. Our investigations on pharmaceutical and tobacco supply chains revealed tremendous data volumes and network traffic, generated from RFID-enabled supply chain networks. It is highly questionable if a single global discovery service is able to cope with such requirements. In this paper, we address the question how distributed discovery services can deliver their service in a Peer-to-Peer (P2P) based manner. For this purpose, we analyzed the applicability of distinctive distribution schemes and present an approach, which enforces a distribution scheme that allows product managers to decide, which discovery service their data is stored at. Furthermore, we present a prototypical implementation that is based on the open source P2P protocol JXTA. Our architecture utilizes an unstructured P2P network coupled with cache optimizations for lowering response times concerning the processing of a query.

In the last 50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory data management is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, and cloud computing are serving as the foundation for the transition of enterprises away from this restrictive model. In this book, we describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT professionals and IT managers, but it is also for senior management who wish to create new business processes by leveraging in-memory computing.

The reunification of online transactional processing and online analytical processing in one single database has been proposed recently as the advent of columnar in-memory databases makes this both viable and feasible. Compared to traditional row-based databases, resource-intensive operations such as the aggregation can now be executed with high performance. This has emerged to the radical approach of eliminating materialized aggregate tables and calculate all aggregations on the fly. This paper contributes by investigating application and data characteristics that have an influence on the aggregation performance in columnar in-memory databases. In particular, we have examined the Availability-to-Promise application that calculates if a desired order can be fulfilled on the requested date and in the requested quantity. We have implemented a prototype that incorporates our identified characteristics and backed up our findings with relevant experiments and real-life customer data.

A number of governments and organizations around the world agree that the first step to address national and international problems such as energy independence, global warming or emergency resilience, is the redesign of electricity networks, known as Smart Grids. Typically, power grids have "broadcasted" power from generation plants to large population of consumers on a suboptimal way. Nevertheless, the fusion of energy delivery networks and digital information networks, along with the introduction of intelligent monitoring systems (Smart Meters) and renewable energies, would enable two- way electricity trading relationships between electricity suppliers and electricity consumers. The availability of real-time information on electricity demand and pricing, would enable suppliers optimizing their delivery systems, while consumers would have the means to minimize their bill by turning on appliances at off-peak hours. The construction of the Smart Grid entails the design and deployment of information networks and systems of unprecedented requirements on storage, real-time event processing and availability. In this paper, a series of system architectures to store and process Smart Meter reading data are explored and compared aiming to establish a solid foundation in which future intelligent systems could be supported.

In this paper, we describe a main memory hybrid database system called HYRISE, which automatically partitions tables into vertical partitions of varying widths depending on how the columns of the table are accessed. For columns accessed as a part of analytical queries (e.g., via sequential scans), narrow partitions perform better, because, when scanning a single column, cache locality is improved if the values of that column are stored contiguously. In contrast, for columns accessed as a part of OLTP-style queries, wider partitions perform better, because such transactions frequently insert, delete, update, or access many of the fields of a row, and co-locating those fields leads to better cache locality. Using a highly accurate model of cache misses, HYRISE is able to predict the performance of different partitionings, and to automatically select the best partitioning using an automated database design algorithm. We show that, on a realistic workload derived from customer applications, HYRISE can achieve a 20% to 400% performance improvement over pure all-column or all-row designs, and that it is both more scalable and produces better designs than previous vertical partitioning approaches for main memory systems.