Virtual reality (VR) applications are increasingly permeating our lives. The immersion provided by VR enables novel interactions with data that would be impossible in conventional environments. Especially regarding multimedia data, VR could overcome existing limitations when browsing videos to find specific scenes. This paper introduces a virtual multimedia drawer, tailored to VR environments, to enable novel ways to interact with videos. A within-subjects design experiment (N=24) was conducted to evaluate the multimedia drawer on user experience, efficiency, and effectiveness. Results show that the multimedia drawer, while taking slightly longer to locate a particular scene within a video for certain types of tasks (i.e., sequence tasks), provides statistically significantly higher levels of enjoyment, novelty, and stimulation and is preferred over conventional timeline-based approaches. Implications of quantitative and qualitative results regarding the design and features of the multimedia drawer as a video browsing method in VR are critically discussed.

This tutorial provides an overview of multimedia retrieval in the context of eXtended Reality (XR), including using virtual and augmented/mixed reality as a user interface for multimedia retrieval, as well as multimedia search tasks addressing content needs for the creation of XR experiences.It will discuss the opportunities and limitations of XR-based search, the evaluation of XR-based multimedia retrieval systems, the demonstration of selected research systems, and open research challenges.

Interactive retrieval with user-friendly and performant interfaces remains a necessity for video retrieval, even in light of significant gains in retrieval performance through multi-modal encoders. In recent years, novel interaction modalities such as virtual reality (VR) and augmented reality (AR) have gained popularity, but the best way to adapt paradigms from traditional retrieval interfaces, especially for result browsing and interaction, remains an open research question. In this paper, we compare two video retrieval interfaces in a controlled setting to gain insight into the differences in video browsing between VR and desktop interfaces. We formulate hypotheses explaining why there might be performance differences between the two interfaces, define metrics to test the hypotheses, and show results based on data gathered at an evaluation campaign. Our results show that VR interfaces can be competitive in browsing performance and indicate that there can even be an advantage when browsing larger result sets in VR.

The harvesting, management, and analysis of thematic document collections is a major challenge in a wide variety of applications. While the criteria for compiling such collections are individual, the entire process is largely standardized. Therefore, it is not efficient to build new systems over and over again to take over these tasks. In this work, we introduce Seer-Dock, a novel and easy-to deploy general-purpose dockerized framework to build a scholarly document harvesting and management system. It is based on CiteSeerX, the most widely used scholarly search engine. Seer-Dock uses docker containers for all components and thus enables its users to rapidly deploy a full-fledged document collection and management system on any operating system platform and tailor it to the specific needs of an application domain. Moreover, it is easy to scale, orchestrate, maintain, and recover. In this resource paper, we introduce the architecture of Seer-Dock and its components. Like its kernel CiteSeerX, Seer-Dock is available under an Apache 2 open source license.

In recent years, Big Data has become an important topic in many areas of our daily lives, including sports. Almost all professional clubs analyze matches to improve the performance of their teams. However, events are still predominantly captured manually, although many sensor-based and video-based tracking systems exist which provide the positions of the players and the ball in real-time. This manual process is tedious and error-prone. In this paper, we present STREAMTEAM-FOOTBALL, an open source football analysis application, that fills this gap. STREAMTEAM-FOOTBALL allows to analyze football matches fully automatically and in real-time on the basis of tracked position data using a data stream analysis approach. Our evaluation confirms the effectiveness of our automated analysis.

Systems evaluations are an important part of empirical research in computer science. Such evaluations encompass the systematic assessment of the run-time characteristics of systems based on one or several parameters. Considering all possible parameter settings is often a very tedious and time-consuming task with many manual activities, or at least the manual creation of evaluation scripts. Ideally, the thorough evaluation of a complete evaluation space can be fully automated. This includes the set-up of the evaluation, its execution, and the subsequent analysis of the results. In this paper, we introduce Chronos, a system for the automation of the entire evaluation workflow. While Chronos has originally been built for database systems evaluations, its generic approach also allows its usage in other domains. We show how Chronos can be deployed for a concrete database evaluation, the comparative performance analysis of different storage engines in MongoDB.

Gestures play an important role in our daily communications. However, recognizing and retrieving gestures in-the-wild is a challenging task which is not explored thoroughly in literature. In this paper, we explore the problem of identifying and retrieving gestures in a large-scale video dataset provided by the computer vision community and based on queries recorded in-the-wild. Our proposed pipeline, I3DEF, is based on the extraction of spatio-temporal features from intermediate layers of an I3D network, a state-of-the-art network for action recognition, and the fusion of the output of feature maps from RGB and optical flow input. The obtained embeddings are used to train a triplet network to capture the similarity between gestures. We further explore the effect of a person and body part masking step for improving both retrieval performance and recognition rate. Our experiments show the ability of I3DEF to recognize and retrieve gestures which are similar to the queries independently of the depth modality. This performance holds both for queries taken from the test data, and for queries using recordings from different people performing relevant gestures in a different setting.

The steady growth of multimedia collections - both in terms of size and heterogeneity - necessitates systems that are able to conjointly deal with several types of media as well as large volumes of data. This is especially true when it comes to satisfying a particular information need, i.e., retrieving a particular object of interest from a large collection. Nevertheless, existing multimedia management and retrieval systems are mostly organized in silos and treat different media types separately. Hence, they are limited when it comes to crossing these silos for accessing objects. In this paper, we present vitrivr, a general-purpose content-based multimedia retrieval stack. In addition to the keyword search provided by most media management systems, vitrivr also exploits the object's content in order to facilitate different types of similarity search. This can be done within and, most importantly, across different media types giving rise to new, interesting use cases. To the best of our knowledge, the full vitrivr stack is unique in that it seamlessly integrates support for four different types of media, namely images, audio, videos, and 3D models.

Big data in sports comes with two closely related challenges: first, the online analysis of continuous data streams to identify characteristic events and second, advanced retrieval in video collections and/or event data that help game analysts to search for characteristic video scenes. For both challenges, dedicated big data stream processing and retrieval systems have been developed. However, there is no infrastructure yet that integrates retrieval and automatic online data stream analysis. In this paper, we close this gap by seamlessly combining STREAMTEAM, our real-time team sports analysis system, and SPORTSENSE, our team sports video retrieval system, to an integrated team sports analysis infrastructure that (i) automatically detects (collaborative) events and generates statistics in real-time based on a continuous stream of raw positions, (ii) visualizes the analysis results in real-time, (iii) stores the analysis results persistently for offline activities, and (iv) leverages the stored analysis results for intuitive sketch-based video retrieval.

Cloud providers are more and more confronted with very diverse and heterogeneous requirements their customers impose on the management of data. First, these requirements stem from service-level agreements that specify a desired degree of availability and a guaranteed latency. As a consequence, Cloud providers replicate data across data centers or availability zones and/or partition data and place it close to the location of their customers. Second, the workload at each Cloud data center or availability zone is diverse and may significantly change over time - e. g., an OLTP workload during regular business hours and OLAP analyzes over night. For this, polystore and multistore databases have recently been introduced as they are intrinsically able to cope with such mixed and varying workloads. While the problem of heterogeneous requirements on data management in the Cloud is either addressed at global level by replicating and partitioning data across data centers or at local level by providing polystore systems in a Cloud data center, there is no integrated solution that leverages the benefits of both approaches. In this paper, we present the Polypheny-DB vision of a distributed polystore system that seamlessly combines replication and partitioning with local polystores and that is able to dynamically adapt all parts of the system when the workload changes. We present the basic building blocks for both parts of the system and we discuss open challenges towards the implementation of the Polypheny-DB vision.

More and more companies move their data to the Cloud which is able to cope with the high scalability and availability demands due to its pay-as-you-go cost model. For this, databases in the Cloud are distributed and replicated across different data centers. According to the CAP theorem, distributed data management is governed by a trade-off between consistency and availability. In addition, the stronger the provided consistency level, the higher is the generated coordination overhead and thus the impact on system performance. Nevertheless, many OLTP applications demand strong consistency and use ROWA(A) for replica synchronization. ROWA(A) protocols eagerly update all (or all available) replicas and thus generate a high overhead for update transactions. In contrast, quorum-based protocols consider only a subset of sites for eager commit. This reduces the overhead for update transactions at the cost of reads, as the latter also need to access several sites. Existing quorum-based protocols do not consider the load of sites when determining the quorums; hence, they are not able to adapt at run-time to load changes. In this paper, we present QuAD, an adaptive quorum-based replication protocol that constructs quorums by dynamically selecting the optimal quorum configuration w.r.t. load and network latency. Our evaluation of QuAD based on Amazon EC2 shows that it considerably outperforms both static quorum protocols and dynamic protocols that neglect site properties in the quorum construction process.

The last years have seen a vast diversification on the database market. In contrast to the “one-size-fits-all” paradigm according to which systems have been designed in the past, today's database management systems (DBMS) are tuned for particular workloads. This has led to DBMSs optimized for high performance, high throughput read/write workloads in online transaction processing (OLTP) and systems optimized for complex analytical queries (OLAP). However, this approach reaches a limit when systems have to deal with mixed workloads that are neither pure OLAP nor pure OLTP workloads. In such cases, multistores are increasingly gaining popularity. Rather than supporting one single database paradigm and addressing one particular workload, multistores encompass several DBMSs that store data in different schemas and allow to route requests on a per-query level to the most appropriate system. In this paper, we introduce the multistore ICARUS. In our evaluation based on a workload that combines OLTP and OLAP elements, we show that ICARUS is able to speed-up queries up to a factor of three by properly routing queries to the best underlying DBMS.

The IMOTION system is a multimodal content-based video search and browsing application offering a rich set of query modes on the basis of a broad range of different features. It is able to scale with the size of the collection due to its underlying flexible polystore called ADAMpro and its very effective retrieval engine Cineast, optimized for multi-feature fusion. IMOTION is simultaneously geared towards precision-focused searches, i.e., known-item search with image or text queries, and recall-focused, exploratory searches. In this demo, we will present the 2017 IMOTION system deployed on the IACC.3 collection consisting of 600 hours of Internet Archive video, which was also used in the TRECVID 2016 Ad-Hoc Video Search and in the 2017 Video Browser Showdown (VBS) challenge in which IMOTION ranked first. Conference attendees will have the chance to interact with the 2017 IMOTION system and quickly solve various retrieval tasks.

Applications deployed in the Cloud usually come with dedicated performance and availability requirements. This can be achieved by replicating data across several sites and/or by partitioning data. Data replication allows to parallelize read requests and thus to decrease data access latency, but induces significant overhead for the synchronization of updates. Partitioning, in contrast, is highly beneficial if all the data accessed by an application is located at the same site, but again necessitates coordination if distributed transactions are needed to serve applications. In this paper, we analyze three protocols for distributed data management in the Cloud, namely Read-One-Write-All-Available (ROWAA), Majority Quorum (MQ) and Data Partitioning (DP)-all in a configuration that guarantees strong consistency. We introduce BEOWULF, a meta protocol based on a comprehensive cost model that integrates the three protocols and that dynamically selects the protocol with the lowest latency for a given workload. In the evaluation, we compare the prediction of the BEOWULF cost model with a baseline evaluation. The results nicely show the effectiveness of the analytical model and the precision in selecting the best suited protocol for a given workload.

Cloud environments usually feature several geographically distributed data centers. In order to increase the scalability of applications, many Cloud providers partition data and distribute these partitions across data centers to balance the load. However, if the partitions are not carefully chosen, it might lead to distributed transactions. This is particularly expensive when applications require strong consistency guarantees. The additional synchronization needed for atomic commitment would strongly impact transaction throughput and could even completely undo the gain that can be achieved by load balancing. Hence, it is beneficial to avoid distributed transactions as much as possible by partitioning the data in such a way that transactions can be executed locally. As access patterns of characteristic transaction workloads may change over time, the partitioning also needs to be dynamically updated. In this paper we introduce Cumulus, an adaptive data partitioning approach which is able to identify characteristic access patterns of transaction mixes, to determine data partitions based on these patterns, and to dynamically re-partition data if the access patterns change. In the evaluation based on the TPC-C benchmark, we show that Cumulus significantly increases the overall system performance in an OLTP setting compared to static data partitioning approaches. Moreover, we show that Cumulus is able to adapt to workload shifts at runtime by generating partitions that match the actual workload and to re-configure the system on the fly.

Most applications deployed in a Cloud require a high degree of availability. For the data layer, this means that data have to be replicated either within a data center or across Cloud data centers. While replication also allows to increase the performance of applications if data is read as the load can be distributed across replica sites, updates need special coordination among the sites and may have an adverse effect on the overall performance. The actual effects of data replication depend on the replication protocol used. While ROWAA (read-one-write-all-available) prefers read operations, quorum-based replication protocols tend to prefer write operations as not all replica sites need to be updated synchronously. In this paper, we provide a detailed evaluation of ROWAA and quorum-based replication protocols in an Amazon AWS Cloud environment on the basis of the TPC-C benchmark and different transaction mixes. The evaluation results for single data center and multi data center environments show that in general the influence of transaction coordination significantly grows with the number of update sites and a growing number of update transactions. However, not all quorum-based protocols are well suited for high update loads as they may create a hot spot that again significantly impacts performance.

With the advent of data Clouds that come with nearly unlimited storage capacity combined with low storage costs, the well-established update-in-place paradigm for data management is more and more replaced by a multi-version approach. Especially in a Cloud environment with several geographically distributed data centers that act as replica sites, this allows to keep old versions of data and thus to provide a rich set of read operations with different semantics (e.g., read most recent version, read version not older than, read data as of, etc.). A combination of multi-version data management, replication, and partitioning allows to redundantly store several or even all versions of data items without significantly impacting each single site. However, in order to avoid that single sites in such partially replicated data Clouds are overloaded when processing archive queries that access old versions, query optimization has to jointly consider version selection and load balancing (site selection). In this paper, we introduce ARCTIC, a novel cost-aware index for version and site selection for a broad range of query types including both fresh data and archive data. We describe in detail the interplay between the different parts of the index and their implementation. Moreover, we present the results of the evaluation of the combined version and replica index in a Cloud environment that shows a significant gain in query throughput compared to a monolithic index.

The tremendous increase of multimedia data in recent years has heightened the need for systems that not only allow to search with keywords, but that also support content-based retrieval in order to effectively and efficiently query large collections. In this paper, we introduce ADAM, a system that is able to store and retrieve multimedia objects by seamlessly combining aspects from databases and information retrieval. ADAM is able to work with both structured and unstructured data and to jointly provide Boolean retrieval and similarity search. To efficiently handle large volumes of data it makes use of a signature-based indexing and the distribution of the collection to multiple shards that are queried in a MapReduce style. We present ADAM in the setting of a sketch-based image retrieval application using the ImageNet collection containing 14 million images.

Searching for scenes in team sport videos is a task that recurs very often in game analysis and other related activities performed by coaches. In most cases, queries are formulated on the basis of specific motion characteristics the user remembers from the video. Providing sketching interfaces for graphically specifying query input is thus a very natural user interaction for a retrieval application. However, the quality of the query (the sketch) heavily depends on the memory of the user and her ability to accurately formulate the intended search query by transforming this 3D memory of the known item(s) into a 2D sketch query. In this paper, we present an auto-suggest search feature that harnesses spatiotemporal data of team sport videos to suggest potential directions containing relevant data during the formulation of a sketch-based motion query. Users can intuitively select the direction of the desired motion query on-the-fly using the displayed visual clues, thus relaxing the need for relying heavily on memory to formulate the query. At the same time, this significantly enhances the accuracy of the results and the speed at which they appear. A first evaluation has shown the effectiveness and efficiency of our approach.

We present SportSense, a system for interactive sports video retrieval using sketch-based motion queries. SportSense is based on sports videos of games, enriched with an overlay of metadata that incorporates spatio-temporal information about various events and movements. We present how sketch-based motion queries are formulated and executed, as well as the use of various intuitive input interfaces to acquire the query object. The system uses spatio-temporal index structures to facilitate interactive response times.

During the last decade, overlay networks based on distributed hash tables have become the de facto standard for data management in Peer-to-Peer (P2P) systems, with Chord being its most prominent representative. Essentially, with its fully decentralized approach, Chord avoids any bottleneck and single point of failure while guaranteeing data to be retrieved in O(log N) hops in a network consisting of N nodes. By optimizing the number of hops for data access, Chord implicitly assumes that all connections between nodes have comparable bandwidth and latency characteristics. However, in heterogeneous, mobile P2P systems that consist of both mobile and fixed nodes, this is not the case. Moreover, due to the mobility of nodes, connection parameters can dynamically change. Especially in mobile P2P applications where low latency for data access is essential, such as in emergency management, routing should aim at reducing the overall latency, rather than the number of hops in the network. In this paper, we present COMPASS, a protocol for efficient data access in heterogeneous mobile Chord-based P2P systems. COMPASS takes into account that the network latency of nodes in a mobile P2P network may significantly differ and thus aims at minimizing the overall latency, even if this necessitates more hops in the network. This is done by probing the network and by maintaining, in addition to Chord's finger table, at each peer a data structure called COMPASS table. We present in detail the initialization and maintenance of the COMPASS table that dynamically adapts to changing node characteristics. Evaluation results show that COMPASS outperforms standard Chord-based routing and reduces the overall latency in heterogeneous P2P networks consisting of fixed and mobile nodes.

We present a novel and innovative user interface for query-by-sketching based image retrieval that exploits emergent interactive paper and digital pen technology. Users can draw sketches with a digital pen on interactive paper in a user-friendly way. The pen is able to capture the stroke vectors and to interactively stream them to the underlying content-based image retrieval (CBIR) system via the pen's Bluetooth interface. We present the integration of interactive paper/digital pen technology with QbS, our CBIR system tailored to Query-by-Sketching, and we demonstrate the use of the paper and pen interface together with QbS for three different collections: MIRFLICKR-25K, a cartoon collection, and a collection of medieval paper watermarks.

With the increasingly growing size of digital image collections, known image search is gaining more and more importance. Especially in collections where individual objects are not tagged with metadata describing their content, content-based image retrieval (CBIR) is a promising approach, but usually suffers from the unavailability of query images that are good enough to express the user's information need. In this paper, we present the QbS system that provides CBIR based on user-drawn sketches. The QbS system combines angular radial partitioning for the extraction of features in the user-provided sketch, taking into account the spatial distribution of edges, and the image distortion model. This combination offers several highly relevant invariances that allow the query sketch to slightly deviate from the searched image in terms of rotation, translation, relative size, and/or unknown objects in the background. To illustrate the benefits of the approach, we present search results from the evaluation of the QbS system on the basis of the MIRFLICKR collection with 25,000 objects and compare the retrieval results of pure metadata-driven approaches, pure content-based retrieval using different sketches, and combinations thereof.

The proliferation of software and hardware sensors which continuously create large amounts of data has significantly facilitated novel types of applications such as healthcare telemonitoring or roadside traffic management. All these applications demand new mechanisms for online processing and analysis of relevant data coming from multiple data streams. Especially telemonitoring applications in healthcare require a high degree of reliability and must be able to be deployed in a distributed environment. We present OSIRIS-SE, an infrastructure for reliable data stream management in a failure-prone distributed setting including resource-limited mobile devices. OSIRIS-SE supports the combination of different data stream operators into stream processes and offers efficient coordinated operator check pointing for the execution of these stream processes. In order to support mobile devices, OSIRIS-SE is able to deal with multiple failures, offers fine-grained reliability at operator level, and supports decentralized stream process orchestration in a peer-to-peer fashion. Moreover, OSIRIS-SE is fully implemented in Java and thus can be run on different platforms. The demo shows the reliable execution of stream processes in a health monitoring application including a wearable ECG sensor, a Bluetooth enabled blood pressure sensor, and a web cam as data sources. Operators are hosted at mobile devices (PDAs, smart phones) of a patient and at a laptop computer which also acts as base station. An important feature of the demo is to show that sensor data can losslessly be processed by seamlessly migrating stream processing to other devices in the network even in case of multiple failures.

Business processes executing in peer-to-peer environments usually invoke Web services on different, independent peers. Although peer-to-peer environments inherently lack global control, some business processes nevertheless require global transactional guarantees, i.e., atomicity and isolation applied at the level of processes. This paper introduces a new decentralized serialization graph testing protocol to ensure concurrency control and recovery in peer-to-peer environments. The uniqueness of the proposed protocol is that it ensures global correctness without relying on a global serialization graph. Essentially, each transactional process is equipped with partial knowledge that allows the transactional processes to coordinate. Globally correct execution is achieved by communication among dependent transactional processes and the peers they have accessed. In case of failures, a combination of partial backward and forward recovery is applied. Experimental results exhibit a significant performance gain over traditional distributed locking-based protocols with respect to the execution of transactions encompassing Web service requests.

Standards like SOAP, WSDL, and UDDI facilitate the proliferation of services. Based on these technologies, processes are a means to combine services to applications and to provide new value-added services. For large information systems, a centralized process engine is no longer appropriate due to limited scalability. Instead, in this paper, we propose a distributed and decentralized process engine that routes process instances directly from one peer to the next. Such a peer-to-peer process execution promises good scalability characteristics since it is able to dynamically balance the load of processes and services among all available service providers. Therefore, navigation costs only accumulate on peers that are directly involved in the execution. However, this requires sophisticated strategies for the replication of meta-data for peer-to-peer process execution. Especially, replication mechanisms should avoid frequent accesses to global information repositories. In our system, called OSIRIS (Open Service Infrastructure for Reliable and Integrated Process Support), we deploy a publish/subscribe-based replication scheme together with freshness predicates to significantly reduce replication costs. This way, OSIRIS can support process-based applications in a dynamically evolving system without limiting scalability and correctness. Experiments have shown very promising results with respect to scalability. In addition, OSIRIS provides a flexible infrastructure that can be extended seamlessly in a modular way. This paper demonstrates the extension towards distributed concurrency control.

Processes are increasingly being used to make complex application logic explicit. Programming using processes has significant advantages but it poses a difficult problem from the system point of view in that the interactions between processes cannot be controlled using conventional techniques. In terms of recovery, the steps of a process are different from operations within a transaction. Each one has its own termination semantics and there are dependencies among the different steps. Regarding concurrency control, the flow of control of a process is more complex than in a flat transaction. A process may, for example, partially roll back its execution or may follow one of several alternatives. In this article, we deal with the problem of atomicity and isolation in the context of processes. We propose a unified model for concurrency control and recovery for processes and show how this model can be implemented in practice, thereby providing a complete framework for developing middleware applications using processes.

In this paper, we propose process locking, a dynamic scheduling protocol based on ideas of ordered shared locks, that allows for the correct concurrent and fault-tolerant execution of transactional processes. Transactional processes are well defined, complex structured collections of transactional services. The process structure comprises flow of control between single process steps and also considers alternatives for failure handling purposes. Moreover, the individual steps of a process may have different termination characteristics, i.e., they cannot be compensated once they have committed. All these constraints have to be taken into consideration when deciding how to interleave processes. However, due to the higher level semantics of processes, standard locking techniques based on shared and exclusive locks on data objects cannot be applied. Yet, process locking addresses both atomicity and isolation simultaneously at the appropriate level, the scheduling of processes, and accounts for the various constraints imposed by processes. In addition, process locking aims at providing a high degree of concurrency while, at the same time, minimizing execution costs. This is done by allowing cascading aborts for rather simple processes white this is prevented for complex, long-running processes within the same framework.

When relational database systems were introduced twenty years ago (1980), they were an infrastructure and main platform for application development. With today's information systems, the database system is a storage manager, far away from the applications. Our vision is that hyperdatabases become available that move up and extend database concepts to a higher level, closer to the applications. A hyperdatabase manages distributed objects and software components as well as workflows, in analogy to a database system that manages data and transactions. In short, hyperdatabases, also called "higher order databases", will provide "higher order data independence", e.g., immunity of applications against changes in the implementation of components and workload transparency. They will be the infrastructure for distributed information systems engineering of the future, and they are an abstraction from the host of current infrastructures and middleware technology. The article elaborates on this vision and outlines concrete projects at ETHZ such as PowerDB, a database cluster project. It shows how an efficient document engine can be built on top of a database cluster. A further project studies transactional process management as a layer on top of database transactions. Image similarity and multimedia components is another project where a hyperdatabase coordinates specialized components such as feature extraction and indexing services in a distributed environment.

The most important phase in e-commerce interactions is the payment, due to the transfer of sensitive information (e.g., credit card numbers). A couple of requirements exist both from the point of view of a customer and from the merchant's perspective. This set of requirements is even enlarged when complex interactions are considered in which a customer purchases goods originating from different merchants within one single e-commerce transaction. We show how all these different requirements of payment interactions can be seamlessly integrated in transactional payment processes. These processes are generated automatically based on the customer's specification of the e-commerce transaction (involved participants, means of payment, etc.). We present the basic structure of such payment processes, how the requirements are mapped into these processes and how they can be generated automatically. Furthermore, we present the architecture of a payment coordinator that has been implemented within the INVENT project. This payment coordinator controls the execution of transactional payment processes, thereby keeping track of the interactions with the various participants.

Composite systems are collections of autonomous, heterogeneous and distributed software applications. In these systems, data dependencies are continuously violated by local operations, and therefore coordination processes are necessary to guarantee overall correctness and consistency. Such coordination processes must be endowed with some form of execution guarantees, which require the participating subsystems to have certain database functionality (such as atomicity of local operations, order preservation, and either compensation of operations or the deferment of their commit). However, this functionality is not present in many applications and must be implemented by a transactional coordination agent coupled with the application. In this paper, we discuss the requirements to be met by the applications and their associated transactional coordination agents. We identify a minimal set of functionalities which the applications must provide in order to participate in transactional coordination processes, and we also discuss how the missing database functionality can be added to arbitrary applications using transactional coordination agents. Then, we identify the structure of a generic transactional coordination agent and provide an implementation example of a transactional coordination agent tailored to SAP R/3.

The unified theory of concurrency control and recovery integrates atomicity and isolation within a common framework, thereby avoiding many of the shortcomings resulting from treating them as orthogonal problems.This theory can be applied to the traditional read/write model as well as to semantically rich operations.In this paper, we extend the unified theory by applying it to generalized process structures, i.e., arbitrary partially ordered sequences of transaction invocations.lJsing the extended unified theory, our goal is to provide a more flexible handling of concurrent processes while allowing: as much parallelism as possible.Unlike in the original unified theory, we take into account that not all activities of a process might be compensatable and the fact that these process structures require transactional properties more general than in traditional ACID transactions.We provide a correctness criterion for transactional processes and identity the key points in which the more flexible structure of transactional processes implies differences from traditional transactions.