The amount of publicly available Spatio-temporal (ST) data is growing daily and possesses an increasing degree of complexity in more and more use cases. Besides spatial queries such as intersection, the requirements of current applications like Digital Twins (DT) go beyond the limits of a single data processing platform and need to combine a variety of queries with filtering ( e.g., k -NN), aggregation (e.g., counting), ranking (e.g., page-rank), clustering (e.g., k-means, ST-DBSCAN) and more, on ST-models. Since existing ST-platforms are highly specialized for a subset of these operations, it seems logical to distribute the data and queries across several of these systems. However, efficient p rocessing a cross d ifferent s ystems i s still a major challenge in polyglot data management and often demands manual query planning. To solve the automatic planning of those complex queries, we present an approach for cross-platform processing of ST-tasks that uses a symmetric join to handle platform heterogeneity and includes a novel algorithm for operator placement based on a latency model. Although the underlying problem is NP-hard and additional network transfers slow down the overall processing time, experiments on real-world tasks for DTs have shown that cross-platform processing can speed up well-known ST-tasks compared to the expensive query reformulations performed by state-of-the-art ST single-platform solutions.

Spatio-temporal trajectories are essential factors for systems used in public transport, social ecology, and many other disciplines where movement is a relevant dynamic process. Each trajectory describes multiple state changes over time, induced by individual decision-making, based on psychological and social factors with physical constraints. Since a crucial factor of such systems is to reason about the potential trajectories in a closed environment, the primary problem is the realistic replication of individual decision making. Mental factors are often uncertain, not available or cannot be observed in reality. Thus, models for data generation must be derived from abstract studies using probabilities. To solve these problems, we present Multi-Agent-Trajectory-Learning (MATL), a state transition model to learn and generate human-like Spatio-temporal trajectory data. MATL combines Generative Adversarial Imitation Learning (GAIL) with a simulation system that uses constraints given by an agent-based model (Aℬℳ). We use GAIL to learn policies in conjunction with the Aℬℳ, resulting in a novel concept of individual decision making. Experiments with standard trajectory predictions show that our approach produces similar results to real-world observations.

Due to the increasing variety of the current database landscape, polyglot data management has become a hot research topic in recent years. The underlying idea is to combine the benefits of different data stores behind a predefined set of common interfaces and thus address use cases that individual stores cannot meet. This can be accomplished using different approaches which vary greatly in terms of capabilities, functionality, and architectural concepts. This tutorial provides a detailed overview of the current state of research in polyglot data management. We motivate its use by showing the high diversity of existing data stores and discussing three use cases in which individual stores are insufficient. Thereafter, we present different taxonomies for classifying polyglot data systems and give a detailed review of a number of selected systems. Finally, we compare these systems based on their features and discuss open challenges that still need to be addressed in future research.

Content delivery networks (CDNs) are critical for minimizing access latency in the Web as they efficiently distribute online resources across the globe. But since CDNs can only be enabled on the scope of entire websites (and not for individual users or user groups), the effects of page speed acceleration are often quantified with potentially skewed before-after comparisons rather than statistically sound A/B tests. We introduce the system Beaconnect for collecting and analyzing Web performance data without being subject to these limitations. Our contributions are threefold. First, Beaconnect is natively compatible with A/B testing Web performance as it is built for a custom browser-based acceleration approach and thus does not rely on traditional CDN technology. Second, we present our continuous aggregation pipeline that achieves sub-minute end-to-end latency. Third, we describe and evaluate a scheme for continuous real-time reporting that is especially efficient for large customers and processes data from over 100 million monthly users at Baqend.

Spatio-temporal data are semantically valuable information used for various analytical tasks to identify spatially relevant and temporally limited correlations within a domain. The increasing availability and data acquisition from multiple sources with their typically high heterogeneity are getting more and more attention. However, these sources often lack interconnecting shared keys, making their integration a challenging problem. For example, publicly available parking data that consist of point data on parking facilities with fluctuating occupancy and static location data on parking spaces cannot be directly correlated. Both data sets describe two different aspects from distinct sources in which parking spaces and fluctuating occupancy are part of the same semantic model object. Especially for ad hoc analytical tasks on integrated models, these missing relationships cannot be handled using join operations as usual in relational databases. The reason lies in the lack of equijoin relationships, comparing for equality of strings and additional overhead in loading data up before processing. This paper addresses the optimization problem of finding suitable partners in the absence of equijoin relations for heterogeneous spatio-temporal data, applicable to ad hoc analytics. We propose a graph-based approach that achieves good recall and performance scaling via hierarchically separating the semantics along spatial, temporal, and domain-specific dimensions. We evaluate our approach using public data, showing that it is suitable for many standard join scenarios and highlighting its limitations.

Traditional databases are optimized for pull-based queries, i.e. they make information available in direct response to client requests. While this access pattern is adequate for mostly static domains, it requires inefficient and slow workarounds (e.g. periodic polling) when clients need to stay up-to-date. Acknowledging reactive and interactive workloads, modern real-time databases such as Firebase, Meteor, and RethinkDB proactively deliver result updates to their clients through push-based real-time queries. However, current implementations are only of limited practical relevance, since they are incompatible with existing technology stacks, fail under heavy load, or do not support complex queries to begin with. To address these issues, we propose the system design InvaliDB which combines linear read and write scalability for real-time queries with superior query expressiveness and legacy compatibility. We compare InvaliDB against competing system designs to emphasize the benefits of our approach that has been serving customers at the Database-as-a-Service company Baqend since July 2017.

Users leave when page loads take too long. This simple fact has complex implications for virtually all modern businesses, because accelerating content delivery through caching is not as simple as it used to be. As a fundamental technical challenge, the high degree of personalization in today's Web has seemingly outgrown the capabilities of traditional content delivery networks (CDNs) which have been designed for distributing static assets under fixed caching times. As an additional legal challenge for services with personalized content, an increasing number of regional data protection laws constrain the ways in which CDNs can be used in the first place. In this paper, we present Speed Kit as a radically different approach for content distribution that combines (1) a polyglot architecture for efficiently caching personalized content with (2) a natively GDPR-compliant client proxy that handles all sensitive information within the user device. We describe the system design and implementation, explain the custom cache coherence protocol to avoid data staleness and achieve Δ-atomicity, and we share field experiences from over a year of productive use in the e-commerce industry.

Traditional databases are optimized for pull-based queries, i.e. they make information available in direct response to client requests. While this access pattern is adequate for mostly static domains, it requires inefficient and slow workarounds (e.g. periodic polling) when clients need to stay up-to-date. Acknowledging reactive and interactive workloads, modern real-time databases such as Firebase, Meteor, and RethinkDB proactively deliver result updates to their clients through push-based real-time queries. However, current implementations are only of limited practical relevance, since they are incompatible with existing technology stacks, fail under heavy load, or do not support complex queries to begin with. To address these issues, we propose the system design InvaliDB which combines linear read and write scalability for real-time queries with superior query expressiveness and legacy compatibility. We compare InvaliDB against competing system designs to emphasize the benefits of our approach. To validate our claims of linear scalability, we further present an experimental evaluation of the InvaliDB prototype that has been serving customers at the Database-as-a-Service company Baqend since July 2017.

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching.

The unprecedented scale at which data is consumed and generated today has shown a large demand for scalable data management and given rise to non-relational, distributed “NoSQL” database systems. Two central problems triggered this process: 1) vast amounts of user-generated content in modern applications and the resulting requests loads and data volumes 2) the desire of the developer community to employ problem-specific data models for storage and querying. To address these needs, various data stores have been developed by both industry and research, arguing that the era of one-size-fits-all database systems is over. The heterogeneity and sheer amount of these systems - now commonly referred to as NoSQL data stores - make it increasingly difficult to select the most appropriate system for a given application. Therefore, these systems are frequently combined in polyglot persistence architectures to leverage each system in its respective sweet spot. This tutorial gives an in-depth survey of the most relevant NoSQL databases to provide comparative classification and highlight open challenges. To this end, we analyze the approach of each system to derive its scalability, availability, consistency, data modeling and querying characteristics. We present how each system's design is governed by a central set of trade-offs over irreconcilable system properties. We then cover recent research results in distributed data management to illustrate that some shortcomings of NoSQL systems could already be solved in practice, whereas other NoSQL data management problems pose interesting and unsolved research challenges.

An important goal of self-managing databases is the autonomic adaptation of the database configuration to evolving workloads. However, the diversity of SQL statements in real-world workloads typically causes the required analysis overhead to be prohibitive for a continuous workload analysis. The workload classification presented in this paper reduces the workload analysis overhead by grouping similar workload events into classes. Our approach employs clustering techniques based upon a general distance function for DBS workload events. To be applicable for a continuous workload analysis, our workload classification specifically addresses a stream-based, lightweight operation, a controllable loss of quality, and self-management.

Cross-organizational business processes gain more and more attention in the scientific community. For modeling these processes, choreographies are an adequate solution to describe the observable behavior for each business participant and the corresponding protocols between them. Nevertheless, current approaches mainly focus on modeling and conformance checking before runtime. In contrast, this contribution concentrates on the runtime validation of choreographies and will thus propose a monitoring infrastructure that supports this validation. We will sketch the requirements for a monitoring infrastructure as well as the solutions in order to meet these requirements. Without such a reliable monitoring framework, a runtime validation framework simply would not be able to correctly validate the interactions.

Current approaches to transactional support of distributed processes in service-oriented environments are limited to scenarios where the participant initiating the process maintains a controlling position throughout the lifetime of the process. This constraint impedes support of complex processes where participants may only possess limited local views on the overall process. In this paper, we present a framework to extend WS-BusinessActivity that strengthens the role of the coordinator. We develop a set of rules for deciding on the confirmation or cancellation of participants' work and introduce protocol extensions for monitoring the progress of a process, allowing coordinators to initiate the completion of processes largely autonomously. We discuss motivating examples as well as existing approaches and evaluate our rule set against various service interaction patterns.

The recently increased amount of information stored in XML format has lead to the development and wide deployment of so-called native XML database management systems (XML DBMS). In parallel, (object-)relational DBMS remain well known, approved and widely used for persistent storage of data. There are many research and industrial areas, including virtual enterprises, Web portals, digital libraries, data management systems, etc., where applications need to manage both (object-) relational DBMS and XML DBMS to retrieve information from these kinds of data sources. This has created a need for integrated access to (object-)relational and XML data sources. The focus of our investigation in this context comprises design and development of an integration middleware between the application and the data sources, allowing unified access to the entire information for SQL- and XML-based applications. In this paper, we propose a query processing technique supporting integrated access to (object-)relational and XML data sources via both query languages, SQL and XQuery. The local data sources under integration can be queried from the corresponding unified global views, an SQL-View (for SQL-based applications) as well as an XML-View (for XML-based applications), both offering access to the entire integrated information

We describe a new benchmark approach which is needed for two reasons. First, today's computer systems almost exclusively use relational database management systems (DBMS), and, besides (purely) relational DB technology, only the new trend of object-relational technology is expected to obtain practical relevance. Second, today's (complex) software systems are developed in an object-oriented way. Thus, a benchmark approach is needed assessing the contributions of (object-)relational DBMS to object-oriented software development. Consequently, such a benchmark does not only have to 'measure' the efficiency of the DBMS itself but does also have to take the overhead to be spent for mapping object-oriented data structures to the DBMS interface into account. We describe how our approach achieves this goal and report on first measurement results.

XML (Extensible Markup Language) is a textual markup language designed for the creation of self-describing documents which contain textual data combined with structural information describing the structure of the textual data. We introduce XCoP (XML Content Repository) as a repository which is based on an object-relational database management system (ORDBMS) and improves content management of XML documents, thereby exploiting their structural information. It allows users to reuse and process the textual portions of document contents, called fragments, which are flexibly configurable. Moreover, it enables collaborative development of documents and facilitates synchronization of fragment modification and versioning. Thus, XCoP offers comprehensive content management functionality by taking advantage of the availability of structural information.

Nowadays the complexity of design processes, no matter which design domain (CAD, software engineering, etc.) they belong to, requires system support by means of so-called repositories. Repositories help managing design artifacts by offering adequate storage and manipulation services. Some of the most important features of a repository are version management and activity management. Versioning comprises the specification, storage, and maintenance of versioned design objects whereas activity management is responsible for cooperation control, designflow management and management of design transactions processing versioned design objects. Regarding these issues (version and activity management) repository technology, as we think, should not only provide pre-defined services, but should be flexible enough to reflect different application needs. For that reason, we propose to provide repository managers by generic methods, i.e. by generating the corresponding functionality. In this paper, we consider a representative cooperation model, which is based on versioning services, in order to identify the major data manipulation and activity control needs of cooperative design applications. We will focus on the data manipulation needs by introducing our generative approach for customizing versioning facilities. Additionally, we will outline our ideas of applying a generative approach also for the provision of tailored activity control services. Thus, the paper wants to show that by exploiting generic methods and reuse as well as the extensibility properties of new object-relational database technology, repository managers can be flexibly tailored to special application needs and, thereby, applications do not have to be forced to deal with systems only providing pre-defined services.

Since the emergence of the World Wide Web in the year 1990, the number of accessible Web servers, HTML pages, and thereby the amount of accessible data have increased dramatically. Consequently many people around the world are engaged in maintaining a Web server and its content. While creating HTML pages is getting more and more simple, the administration of a Web site is still a time consuming and demanding task. Available tools for Web Content Management (WCM) only cover some of the relevant aspects of the whole task; an integrated fully functional solution is missing. Therefore, Web administrators still have to cope with many problems, such as ensuring validity of available documents or keeping hyperlinks consistent. We present administrative components of iWebDB, an integrated WCM solution, based on object-relational database technology. By extending an object-relational database system (ORDBS) with WCM functionality, many tasks of WCM, such as validity checks and consistency checks for local hyperlinks, can be delegated to the DBS and thereby automated. Therefore, iWebDB achieves more effective and easier administration of a Web server and its content by exploiting the extensibility property of ORDBSs.

'Computer-Supported Cooperative Work' is a young research area considering applications with strong demands on database technology. Design applications need support for cooperation and some means for controlling their inherent dynamics. However, today's CAD systems consisting of a collection of diverse design tools typically do not support these requirements. Therefore, an encompassing processing model is needed that covers the overall design process in general as well as CAD-tool application in particular. The CONCORD model described in the paper, reflects the distinct properties of design process dynamics by distinguishing three levels of abstraction. The highest level supports application-specific cooperation control and design process administration, the second considers goal-oriented tool invocation and work-flow management while the third level provides tool processing of design data. To achieve level-spanning control, the authors rely on transactional facilities provided at the various system layers.>