Viliam Simko

A prediction-driven adaptation approach for self-adaptive sensor networks

Engineering self-adaptive software in unpredictable environments such as pervasive systems, where networks ability, remaining battery power and environmental conditions may vary over the lifetime of the system is a very challenging task. Many current software engineering approaches leverage run-time architectural models to ease the design of the autonomic control loop of these self-adaptive systems. While these approaches perform well in reacting to various evolutions of the runtime environment, implementations based on reactive paradigms have a limited ability to anticipate problems, leading to transient unavailability of the system, useless costly adaptations, or resources waste. In this paper, we follow a proactive self-adaptation approach that aims at overcoming the limitation of reactive approaches. Based on predictive analysis of internal and external context information, our approach regulates new architecture reconfigurations and deploys them using models at runtime. We have evaluated our approach on a case study where we combined hourly temperature readings provided by National Climatic Data Center (NCDC) with fire reports from Moderate Resolution Imaging Spectroradiometer (MODIS) and simulated the behavior of multiple systems. The results confirm that our proactive approach outperforms a typical reactive system in scenarios with seasonal behavior.

From Textual Use-Cases to Component-Based Applications

A common practice to capture functional requirements of a software system is to utilize use-cases, which are textual descriptions of system usage scenarios written in a natural language. Since the substantial information about the system is captured by the use-cases, it comes as a natural idea to generate from these descriptions the implementation of the system (at least partially). However, the fact that the use-cases are in a natural language makes this task extremely difficult. In this paper, we describe a model-driven tool allowing code of a system to be generated from use-cases in plain English. The tool is based on the model-driven development paradigm, which makes it modular and extensible, so as to allow for use-cases in multiple language styles and generation for different component frameworks.

Formal Verification of Annotated Textual Use-Cases

1Department of Distributed and Dependable Systems, Faculty of Mathematics and Physics, Charles University, Malostranské náměstí 25, 11800 Prague 1, Czech Republic 2Institute for Program Structures and Data Organisation, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany 3Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 18207 Prague 8, Czech Republic ∗Corresponding author: simko@d3s.mff.cuni.cz

Crowd Sensing of Road Conditions and Its Monetary Implications on Vehicle Navigation

This paper quantifies the monetary impact of using road roughness data for path planning. Using a crowd-based data source, a vehicle cost model we performed a sensitivity analysis to investigate the monetary implications on vehicle owners. The results are presented as a collection of trade-off matrices showing potential yearly cost savings for different car types, road roughness levels. Moreover, the dependency between fuel price, overall cost savings is presented. Although the cost savings depend on vehicle type, on the fuel costs, our results show that the main factor is the amount of road segments with high roughness index. In particular, car owners can benefit from rerouting to a smoother road profile only in regions with road roughness at least IRI ~ 4 m/km.

Recovering Traceability Links Between Code and Specification Through Domain Model Extraction

Requirements traceability is an extremely important aspect of software development and especially of maintenance. Efficient maintaining of traceability links between high-level requirements specification and low-level implementation is hindered by many problems. In this paper, we propose a method for automated recovery of links between parts of the textual requirement specification and the source code of implementation. The described method is based on a method allowing extraction of a prototype domain model from plain text requirements specification. The proposed method is evaluated on two non-trivial examples. The performed experiments show that our method is able to link requirements with source code with the accuracy of \(F_1=58-61\,\%\).

OASIS Archive --- Open Archiving System with Internet Sharing

OASIS Archive project aimed at developing a system for the universal presentation of Media Art works independent of location. The goal was to establish a user-friendly search system in order to ensure the preservation and availability (sustainability) of cultural heritage in the field of Media Art. The metadata system interlinking databases of all participating institutions can be accessed by individual users (both researchers and general public) through an on-line interface, by multimedia archive servers engaged in exchange within a distributed system and by various play out media. Access permission is scalable depending on whether the access takes place within the system or from the outside.

Open internet gateways to archives of media art

The EU-projects OASIS Archive and GAMA aimed to develop systems for the common presentation of distributed Media Art works, independent of their location. The paper presents the technical solutions implemented during projects, and contextualise the work in the related media art scene. In order to ensure the preservation and availability (sustainability) of cultural heritage, the metadata systems of all participating institutions were interlinked and they can now be accessed by individual users (both researchers and general public) through an on-line interface. The developed multimedia archive servers enable the exchange of metadata within a distributed system and enable various play-out sources and media. The decentralised architecture, where data can remain at their physical location and are linked at the metadata level, is the key concept of the presented system.

Verifying Temporal Properties of Use-Cases in Natural Language

This paper presents a semi-automated method that helps iteratively write use-cases in natural language and verify consistency of behavior encoded within them. In particular, this is beneficial when the use-cases are created simultaneously by multiple developers. The proposed method allows verifying the consistency of textual use-case specification by employing annotations in use-case steps that are transformed into temporal logic formulae and verified within a formal behavior model. A supporting tool for plain English use-case analysis is currently being enhanced by integrating the verification algorithm proposed in the paper.

Enabling crowdsensing-based road condition monitoring service by intermediary

Constant monitoring of road conditions would be beneficial for road authorities as well as road users. However, this is currently not possible due to limited resources. This is because road condition monitoring is carried out by engineering companies using limited resources such as specialized vehicles and trained personnel. The ubiquity of smart devices carried by drivers, such as smartphones and the ever-increasing number of sensors installed in modern vehicles, makes it possible to provide information about the condition of the road on which the vehicle is driving. We develop a smart, crowd-based road condition monitoring service that establishes an intermediary between the crowd as data provider and the road authorities and road users as service customers. In addition to providing customers with accurate and frequent road condition information, subscribers can monetize their collected data. We prove the feasibility and usability of this smart service through analytical and descriptive evaluations.

Taming the Evolution of Big Data and its Technologies in BigGIS - A Conceptual Architectural Framework for Spatio-Temporal Analytics at Scale.

In the era of spatio-temporal big data, geographic information systems have to deal with a myriad of big data induced challenges such as scalability, flexibility or fault-tolerance. Furthermore, the rapid evolution of the underlying, occasionally competing big data ecosystems inevitably needs to be taken into account from the early system design phase. In order to generate valuable knowledge from spatio-temporal big data, a holistic approach manifested in an appropriate architectural design is necessary, which is a non-trivial task with regards to the tremendous design space. Therefore, we present the conceptual architectural framework of BigGIS, a predictive and prescriptive spatio-temporal analytics platform, that integrates big data analytics, semantic web technologies and visual analytics methodologies in our continuous refinement model.