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Many database management systems (DBMS) need to ensure atomicity and isolation of transactions for logical data consistency, as well as to guarantee temporal correctness of the executed transactions. Since the mechanisms for atomicity and isolation may lead to breaching temporal correctness, trade-offs between these properties are often required during the DBMS design. To be able to address this concern, we have previously proposed the pattern-based UPPCART framework, which models the transactions and the DBMS mechanisms as timed automata, and verifies the trade-offs with provable guarantee. However, the manual construction of UPPCART models can require considerable effort and is prone to errors. In this paper, we advance the formal analysis of atomic concurrent real-time transactions with tool-automated construction of UPPCART models. The latter are generated automatically from our previously proposed UTRAN specifications, which are high-level UML-based specifications familiar to designers. To achieve this, we first propose formal definitions for the modeling patterns in UPPCART, as well as for the pattern-based construction of DBMS models, respectively. Based on this, we establish a translational semantics from UTRAN specifications to UPPCART models, to provide the former with a formal semantics relying on timed automata, and develop a tool that implements the automated transformation. We also extend the expressiveness of UTRAN and UPPCART, to incorporate transaction sequences and their timing properties. We demonstrate the specification in UTRAN, automated transformation to UPPCART, and verification of the traded-off properties, via an industrial use case.

Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things (IoT) systems, many with timing constraints. Understanding the common and variable features of data aggregation processes, especially their implications to the timerelated properties, is key to improving the quality of the designed system and reduce design effort. In this paper, we present a survey of data aggregation processes in a variety of application domains from literature.We investigate their common and variable features, which serves as the basis of our previously proposed taxonomy called DAGGTAX. By studying the implications of the DAGGTAX features, we formulate a set of constraints to be satisfied during design, which helps to check the correctness of the specifications and reduce the design space. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. We apply DAGGTAX on industrial case studies, showing that DAGGTAX not only strengthens the understanding, but also serves as the foundation of a design tool which facilitates the model-driven design of data aggregation processes.

Many industrial control systems manage critical data using Database Management Systems (DBMS). The correctness of transactions, especially their atomicity, isolation and temporal correctness, is essential for the dependability of the entire system. Existing methods and techniques, however, either lack the ability to analyze the interplay of these properties, or do not scale well for systems with large amounts of transactions and data, and complex transaction management mechanisms. In this paper, we propose to analyze large scale real-time database systems using statistical model checking. We propose a pattern-based framework, by extending our previous work, to model the real-time DBMS as a network of stochastic timed automata, which can be analyzed by UPPAAL Statistical Model Checker. We present an industrial case study, in which we design a collision avoidance system for multiple autonomous construction vehicles, via concurrency control of a real-time DBMS. The desired properties of the designed system are analyzed using our proposed framework.

Efficient monitoring of a cloud system involves multiple aggregation processes and large amounts of data with various and interdependent requirements. A thorough understanding and analysis of the characteristics of data aggregation processes can help to improve the software quality and reduce development cost. In this paper, we propose a systematic approach for designing data aggregation processes in cloud monitoring systems. Our approach applies a feature-oriented taxonomy called DAGGTAX (Data AGGregation TAXonomy) to systematically specify the features of the designed system, and SAT-based analysis to check the consistency of the specifications. Following our approach, designers first specify the data aggregation processes by selecting and composing the features from DAGGTAX. These specified features, as well as design constraints, are then formalized as propositional formulas, whose consistency is checked by the Z3 SAT solver. To support our approach, we propose a design tool called SAFARE (SAt-based Feature-oriented dAta aggREgation design), which implements DAGGTAX-based specification of data aggregation processes and design constraints, and integrates the state-of-the-art solver Z3 for automated analysis. We also propose a set of general design constraints, which are integrated by default in SAFARE. The effectiveness of our approach is demonstrated via a case study provided by industry, which aims to design a cloud monitoring system for video streaming. The case study shows that DAGGTAX and SAFARE can help designers to identify reusable features, eliminate infeasible design decisions, and derive crucial system parameters.

Concurrency control faults may lead to unwanted interleavings, and breach data consistency in distributed transaction systems. However, due to the unpredictable delays between sites, detecting concurrency control faults in distributed transaction systems is difficult. In this paper, we propose a methodology, relying on model-based testing and mutation testing, for designing test cases in order to detect such faults. The generated test inputs are designated delays between distributed operations, while the outputs are the occurrence of unwanted interleavings that are consequences of the concurrency control faults. We mutate the distributed transaction specification with common concurrency control faults, and model them as UPPAAL timed automata, in which designated delays are encoded as stopwatches. Test cases are generated via reachability analysis using UPPAAL Model Checker, and are selected to form an effective test suite. Our methodology can reduce redundant test cases, and find the appropriate delays to detect concurrency control faults effectively.

Although atomicity, isolation and temporal correctness are crucial to the dependability of many real-time database-centric systems, the selected assurance mechanism for one property may breach another. Trading off these properties requires to specify and analyze their dependencies, together with the selected supporting mechanisms (abort recovery, concurrency control, and scheduling), which is still insufficiently supported. In this paper, we propose a UML profile, called UTRAN, for specifying atomic concurrent real-time transactions, with explicit support for all three properties and their supporting mechanisms. We also propose a pattern-based modeling framework, called UPPCART, to formalize the transactions and the mechanisms specified in UTRAN, as UPPAAL timed automata. Various mechanisms can be modeled flexibly using our reusable patterns, after which the desired properties can be verified by the UPPAAL model checker. Our techniques facilitate systematic analysis of atomicity, isolation and temporal correctness trade-offs with guarantee, thus contributing to a dependable real-time database system.

Real-time DataBase Management Systems (RTDBMS) have been considered as a promising means to manage data for data-centric automotive systems. During the design of an RTDBMS, one must carefully trade off data consistency and timeliness, in order to achieve an acceptable level of both properties. Previously, we have proposed a design process called DAGGERS to facilitate a systematic customization of transaction models and decision on the run-time mechanisms. In this paper, we evaluate the applicability of DAGGERS via an industrially relevant case study that aims to design the transaction management for an on-board diagnostic system, which should guarantee both timeliness and data consistency under concurrent access. To achieve this, we apply the pattern-based approach of DAGGERS to formalize the transactions, and derive the appropriate isolation level and concurrency control algorithm guided by model checking. We show by simulation that the implementation of our designed system satisfies the desired timeliness and derived isolation, and demonstrate that DAGGERS helps to customize desired real-time transaction management prior to implementation.

Data aggregation processes are essential constituents in many data management applications. Due to their complexity, designing data aggregation processes often demands considerable efforts. A study on the features of data aggregation processes will provide a comprehensive view for the designers and ease the design process. Existing works either propose application-specific aggregation solutions, or focus on particular aspects of aggregation processes such as aggregate functions, hence they do not offer a high-level, generic description. In this paper, we propose a taxonomy of data aggregation processes called DAGGTAX, which builds on the results of an extensive survey within various application domains. Our work focuses on the features of aggregation processes and their implications, especially on the temporal data consistency and the process timeliness. We present our taxonomy as a feature diagram, which is a visual notation with formal semantics. The taxonomy can then serve as the foundation of a design tool that enables designers to build an aggregation process by selecting and composing desired features. Based on the implications of the features, we formulate three design rules that eliminate infeasible feature combinations. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. 

Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things (IoT) systems. Due to the heterogeneity and real-time constraints in such systems, designing appropriate data aggregation processes often demands considerable effort. A study on the characteristics of data aggregation processes is then desirable, as it provides a comprehensive view of such processes, potentially facilitating their design, as well as the development of tool support to aid designers. In this paper, we propose a taxonomy called DAGGTAX, which is a feature diagram that models the common and variable characteristics of data aggregation processes, with a special focus on the real-time aspect. The taxonomy can serve as the foundation of a design tool, which we also introduce, enabling designers to build an aggregation process by selecting and composing desired features, and to reason about the feasibility of the design. We apply DAGGTAX on industrial case studies, showing that DAGGTAX not only strengthens the understanding, but also facilitates the model-driven design of data aggregation processes. © 2017, Springer International Publishing AG.