62 research outputs found
Sound Atomicity Inference for Data-Centric Synchronization
Data-Centric Concurrency Control (DCCC) shifts the reasoning about
concurrency restrictions from control structures to data declaration. It is a
high-level declarative approach that abstracts away from the actual concurrency
control mechanism(s) in use. Despite its advantages, the practical use of DCCC
is hindered by the fact that it may require many annotations and/or multiple
implementations of the same method to cope with differently qualified
parameters. Moreover, the existing DCCC solutions do not address the use of
interfaces, precluding their use in most object-oriented programs. To overcome
these limitations, in this paper we present AtomiS, a new DCCC model based on a
rigorously defined type-sound programming language. Programming with AtomiS
requires only (atomic)-qualifying types of parameters and return values in
interface definitions, and of fields in class definitions. From this atomicity
specification, a static analysis infers the atomicity constraints that are
local to each method, considering valid only the method variants that are
consistent with the specification, and performs code generation for all valid
variants of each method. The generated code is then the target for automatic
injection of concurrency control primitives, by means of the desired automatic
technique and associated atomicity and deadlock-freedom guarantees, which can
be plugged-into the model's pipeline. We present the foundations for the AtomiS
analysis and synthesis, with formal guarantees that the generated program is
well-typed and that it corresponds behaviourally to the original one. The
proofs are mechanised in Coq. We also provide a Java implementation that
showcases the applicability of AtomiS in real-life programs
Letters from the War of Ecosystems – An Analysis of Independent Software Vendors in Mobile Application Marketplaces
The recent emergence of a new generation of mobile application marketplaces has changed the business in the mobile ecosystems. The marketplaces have gathered over a million applications by hundreds of thousands of application developers and publishers. Thus, software ecosystems—consisting of developers, consumers and the orchestrator—have emerged as a part of the mobile ecosystem.
This dissertation addresses the new challenges faced by mobile application developers in the new ecosystems through empirical methods. By using the theories of two-sided markets and business ecosystems as the basis, the thesis assesses monetization and value creation in the market as well as the impact of electronic Word-of-Mouth (eWOM) and developer multihoming— i. e. contributing for more than one platform—in the ecosystems. The data for the study was collected with web crawling from the three biggest marketplaces: Apple App Store, Google Play and Windows Phone Store.
The dissertation consists of six individual articles. The results of the studies show a gap in monetization among the studied applications, while a majority of applications are produced by small or micro-enterprises. The study finds only weak support for the impact of eWOM on the sales of an application in the studied ecosystem. Finally, the study reveals a clear difference in the multi-homing rates between the top application developers and the rest. This has, as discussed in the thesis, an impact on the future market analyses—it seems that the smart device market can sustain several parallel application marketplaces.
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Muutama vuosi sitten julkistetut uuden sukupolven mobiilisovellusten kauppapaikat ovat muuttaneet mobiiliekosysteemien liiketoimintadynamiikkaa. Nämä uudet markkinapaikat ovat jo onnistuneet houkuttelemaan yli miljoona sovellusta sadoilta tuhansilta ohjelmistokehittäjiltä. Nämä kehittäjät yhdessä markkinapaikan organisoijan sekä loppukäyttäjien kanssa ovat muodostaneet ohjelmistoekosysteemin osaksi laajempaa mobiiliekosysteemiä.
Tässä väitöskirjassa tarkastellaan mobiilisovellusten kehittäjien uudenlaisilla kauppapaikoilla kohtaamia haasteita empiiristen tutkimusmenetelmien kautta. Väitöskirjassa arvioidaan sovellusten monetisaatiota ja arvonluontia sekä verkon asiakasarviointien (engl. electronicWord-of-Mouth, eWOM) ja kehittäjien moniliittymisen (engl. multi-homing) — kehittäjä on sitoutunut useammalle kuin yhdelle ekosysteemille — vaikutuksia ekosysteemissä. Työn teoreettinen tausta rakentuu kaksipuolisten markkinapaikkojen ja liiketoimintaekosysteemien päälle. Tutkimuksen aineisto on kerätty kolmelta suurimmalta mobiilisovellusmarkkinapaikalta: Apple App Storesta, Google Playstä ja Windows Phone Storesta.
Tämä artikkeliväitöskirja koostuu kuudesta itsenäisestä tutkimuskäsikirjoituksesta. Artikkelien tulokset osoittavat puutteita monetisaatiossa tutkittujen sovellusten joukossa. Merkittävä osa tarkastelluista sovelluksista on pienten yritysten tai yksittäisten kehittäjien julkaisemia. Tutkimuksessa löydettiin vain heikkoa tukea eWOM:in positiiviselle vaikutukselle sovellusten myyntimäärissä. Työssä myös osoitetaan merkittävä ero menestyneimpien sovelluskehittäjien sekä muiden kehittäjien moniliittymiskäyttäytymisen välillä. Tällä havainnolla on merkitystä tuleville markkina-analyyseille ja sen vaikutuksia on käsitelty työssä. Tulokset esimerkiksi viittaavat siihen, että markkinat pystyisivät ylläpitämään useita kilpailevia kauppapaikkoja.</p
The Impact of Digital Technologies on Public Health in Developed and Developing Countries
This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters
This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p
Spark solutions for discovering fuzzy association rules in Big Data
The research reported in this paper was partially supported the COPKIT project from the 8th Programme Framework (H2020) research and innovation programme (grant agreement No 786687) and from the BIGDATAMED projects with references B-TIC-145-UGR18 and P18-RT-2947.The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.COPKIT project from the 8th Programme Framework (H2020) research and innovation programme 786687BIGDATAMED projects B-TIC-145-UGR18
P18-RT-294
Improving Access and Mental Health for Youth Through Virtual Models of Care
The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial
Investigating business process elements: a journey from the field of Business Process Management to ontological analysis, and back
Business process modelling languages (BPMLs) typically enable the representation of business processes via the creation of process models, which are constructed using the elements and graphical symbols of the BPML itself. Despite the wide literature on business process modelling languages, on the comparison between graphical components of different languages, on the development and enrichment of new and existing notations, and the numerous definitions of what a business process is, the BPM community still lacks a robust (ontological) characterisation of the elements involved in business process models and, even more importantly, of the very notion of business process. While some efforts have been done towards this direction, the majority of works in this area focuses on the analysis of the behavioural (control flow) aspects of process models only, thus neglecting other central modelling elements, such as those denoting process participants (e.g., data objects, actors), relationships among activities, goals, values, and so on.
The overall purpose of this PhD thesis is to provide a systematic study of the elements that constitute a business process, based on ontological analysis, and to apply these results back to the Business Process Management field. The major contributions that were achieved in pursuing our overall purpose are: (i) a first comprehensive and systematic investigation of what constitutes a business process meta-model in literature, and a definition of what we call a literature-based business process meta-model starting from the different business process meta-models proposed in the literature; (ii) the ontological analysis of four business process elements (event, participant, relationship among activities, and goal), which were identified as missing or problematic in the literature and in the literature-based meta-model; (iii) the revision of the literature-based business process meta-model that incorporates the analysis of the four investigated business process elements - event, participant, relationship among activities and goal; and (iv) the definition and evaluation of a notation that enriches the relationships between activities by including the notions of occurrence dependences and rationales
Decision-Making with Multi-Step Expert Advice on the Web
This thesis deals with solving multi-step tasks by using advice from experts, which are algorithms to solve individual steps of such tasks. We contribute with methods for maximizing the number of correct task solutions by selecting and combining experts for individual task instances and methods for automating the process of solving tasks on the Web, where experts are available as Web services.
Multi-step tasks frequently occur in Natural Language Processing (NLP) or Computer Vision, and as research progresses an increasing amount of exchangeable experts for the same steps are available on the Web. Service provider platforms such as Algorithmia monetize expert access by making expert services available via their platform and having customers pay for single executions.
Such experts can be used to solve diverse tasks, which often consist of multiple steps and thus require pipelines of experts to generate hypotheses.
We perceive two distinct problems for solving multi-step tasks with expert services: (1) Given that the task is sufficiently complex, no single pipeline generates correct solutions for all possible task instances. One thus must learn how to construct individual expert pipelines for individual task instances in order to maximize the number of correct solutions, while also taking into account the costs adhered to executing an expert. (2) To automatically solve multi-step tasks with expert services, we need to discover, execute and compose expert pipelines. With mostly textual descriptions of complex functionalities and input parameters, Web automation entails to integrate available expert services and data, interpreting user-specified task goals or efficiently finding correct service configurations.
In this thesis, we present solutions to both problems: (1) We enable to learn well-performing expert pipelines assuming available reference data sets (comprising a number of task instances and solutions), where we distinguish between centralized and decentralized decision-making. We formalize the problem as specialization of a Markov Decision Process (MDP), which we refer to as Expert Process (EP) and integrate techniques from Statistical Relational Learning (SRL) or Multiagent coordination. (2) We develop a framework for automatically discovering, executing and composing expert pipelines by exploiting methods developed for the Semantic Web. We lift the representations of experts with structured vocabularies modeled with the Resource Description Framework (RDF) and extend EPs to Semantic Expert Processes (SEPs) to enable the data-driven execution of experts in Web-based architectures.
We evaluate our methods in different domains, namely Medical Assistance with tasks in Image Processing and Surgical Phase Recognition, and NLP for textual data on the Web, where we deal with the task of Named Entity Recognition and Disambiguation (NERD)
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