485 research outputs found
Configuration Management of Distributed Systems over Unreliable and Hostile Networks
Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems.
This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration.
Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture.
The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn.
Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts
Chatbots for Modelling, Modelling of Chatbots
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202
Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives
Collectiveness is an important property of many systems--both natural and
artificial. By exploiting a large number of individuals, it is often possible
to produce effects that go far beyond the capabilities of the smartest
individuals, or even to produce intelligent collective behaviour out of
not-so-intelligent individuals. Indeed, collective intelligence, namely the
capability of a group to act collectively in a seemingly intelligent way, is
increasingly often a design goal of engineered computational systems--motivated
by recent techno-scientific trends like the Internet of Things, swarm robotics,
and crowd computing, just to name a few. For several years, the collective
intelligence observed in natural and artificial systems has served as a source
of inspiration for engineering ideas, models, and mechanisms. Today, artificial
and computational collective intelligence are recognised research topics,
spanning various techniques, kinds of target systems, and application domains.
However, there is still a lot of fragmentation in the research panorama of the
topic within computer science, and the verticality of most communities and
contributions makes it difficult to extract the core underlying ideas and
frames of reference. The challenge is to identify, place in a common structure,
and ultimately connect the different areas and methods addressing intelligent
collectives. To address this gap, this paper considers a set of broad scoping
questions providing a map of collective intelligence research, mostly by the
point of view of computer scientists and engineers. Accordingly, it covers
preliminary notions, fundamental concepts, and the main research perspectives,
identifying opportunities and challenges for researchers on artificial and
computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for
publication in the Artificial Life journal. Data: 34 pages, 2 figure
Explainable Predictive and Prescriptive Process Analytics of customizable business KPIs
Recent years have witnessed a growing adoption of machine learning techniques
for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring.
Predictive analytics leverages machine learning and data analytics techniques to predict the future outcome of a process based on historical data. Therefore, the goal of predictive analytics is to identify future trends, and discover potential issues and anomalies in the process before they occur, allowing organizations to take proactive measures to prevent them from happening, optimizing the overall performance of the process.
Prescriptive analytics systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running process in order to improve the outcome of a process, which can be defined in various ways depending on the business goals; this can involve measuring process-specific Key Performance Indicators (KPIs), such as costs, execution times, or customer satisfaction, and using this data to make informed decisions about how to optimize the process.
This Ph.D. thesis research work has focused on predictive and prescriptive analytics, with particular emphasis on providing predictions and recommendations that are explainable and comprehensible to process actors.
In fact, while the priority remains on giving accurate predictions and recommendations, the process actors need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way and they need to be convinced that the recommended actions are the most suitable ones to maximize the KPI of interest; otherwise, users would not trust and follow the provided predictions and recommendations, and the predictive technology would not be adopted.Recent years have witnessed a growing adoption of machine learning techniques
for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring.
Predictive analytics leverages machine learning and data analytics techniques to predict the future outcome of a process based on historical data. Therefore, the goal of predictive analytics is to identify future trends, and discover potential issues and anomalies in the process before they occur, allowing organizations to take proactive measures to prevent them from happening, optimizing the overall performance of the process.
Prescriptive analytics systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running process in order to improve the outcome of a process, which can be defined in various ways depending on the business goals; this can involve measuring process-specific Key Performance Indicators (KPIs), such as costs, execution times, or customer satisfaction, and using this data to make informed decisions about how to optimize the process.
This Ph.D. thesis research work has focused on predictive and prescriptive analytics, with particular emphasis on providing predictions and recommendations that are explainable and comprehensible to process actors.
In fact, while the priority remains on giving accurate predictions and recommendations, the process actors need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way and they need to be convinced that the recommended actions are the most suitable ones to maximize the KPI of interest; otherwise, users would not trust and follow the provided predictions and recommendations, and the predictive technology would not be adopted
Design Patterns for Situated Visualization in Augmented Reality
Situated visualization has become an increasingly popular research area in
the visualization community, fueled by advancements in augmented reality (AR)
technology and immersive analytics. Visualizing data in spatial proximity to
their physical referents affords new design opportunities and considerations
not present in traditional visualization, which researchers are now beginning
to explore. However, the AR research community has an extensive history of
designing graphics that are displayed in highly physical contexts. In this
work, we leverage the richness of AR research and apply it to situated
visualization. We derive design patterns which summarize common approaches of
visualizing data in situ. The design patterns are based on a survey of 293
papers published in the AR and visualization communities, as well as our own
expertise. We discuss design dimensions that help to describe both our patterns
and previous work in the literature. This discussion is accompanied by several
guidelines which explain how to apply the patterns given the constraints
imposed by the real world. We conclude by discussing future research directions
that will help establish a complete understanding of the design of situated
visualization, including the role of interactivity, tasks, and workflows.Comment: To appear in IEEE VIS 202
Swarm-Based Drone-as-a-Service for Delivery
There has been a growing interest in the applications of drones as a cost-effective, efficient, and environmentally friendly alternative in various domains. Particularly in the context of delivery services, the demand for contactless and efficient delivery solutions has surged. Drone delivery offers faster and greener deliveries. However, existing methods focus primarily on point-to-point delivery, limiting their potential for optimisation. This thesis proposes a novel approach to servitise drone delivery by operating through a skyway network composed of building rooftops, enabling drones to traverse between source and destination while recharging at intermediate nodes. Although single drone delivery offers numerous advantages, it faces significant challenges in scenarios where multiple packages require simultaneous delivery. Flight regulations, which often limit the carrying capacity of individual drones, necessitate the exploration of alternative solutions. Therefore, this thesis presents a novel Swarm-Based Drone-as-a-Service (SDaaS) model and framework for multiple package delivery. The proposed framework prioritises the composition of services that optimise Quality of Service (QoS) factors, such as delivery time and energy consumption. This thesis identifies swarm-specific constraints and leverages the unique characteristics of drone swarms. It explores swarm formations, in-flight wireless charging between drones, and allocation problems to maximise drone utilisation for consumer deliveries. Furthermore, this research investigates the recommendation of services to consumers based on their preferences, aiming to increase their satisfaction. Moreover, the framework addresses the resilience of SDaaS by addressing issues related to drone soft failures and their impact on other swarm members. Ultimately, this work paves the way for the widespread adoption and optimisation of swarm-based drone services in the context of last-mile delivery
Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices
Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is applied to event-driven microservices, a specific type of cloud-native applications that employ distributed stream processing frameworks to scale with massive data volumes. Extensive experimental evaluations benchmark and compare the scalability of various stream processing frameworks under different configurations and deployments, including different public and private cloud environments. These experiments show that the presented benchmarking method provides statistically sound results in an adequate amount of time. In addition, three case studies demonstrate that the Theodolite benchmarking method can be applied to a wide range of applications beyond stream processing
Bridging the Gap at Ecosystem Level : Enhancing Business Model Innovation in Internet of Things-Enabled Platform Ecosystems
Digitaalinen murros haastaa yrityksiä ja yhteisöjä tarjoamaan innovatiivisia palveluita asiakkailleen ja lisäämään omaa kannattavuuttaan uusia liiketoimintamalleja luomalla. Esineiden internet (IoT) on tunnistettu potentiaaliseksi uudenlaisen arvon mahdollistajaksi. Odotetuista hyödyistä huolimatta onnistuneesti toteutettuja IoT:llä varustettuja alustaekosysteemejä on toistaiseksi vähän. IoT-tutkimus on pääosin keskittynyt teknologisten edistysaskeleiden ottamiseen, kun taas liiketoimintamallien innovaatioiden merkitys on suurelta osin sivuutettu. On kuitenkin muistettava, että teknologian onnistunut käyttöönotto on suurelta osin kiinni hyvin määritellystä liiketoimintamallista ja sen arvolupauksen onnistuneisuudesta
Sosiaalisen vaihdannan teoria (SET) on olennainen IoT:llä varustettujen alustaekosysteemien kontekstissa. Sen mukaan toimijoiden tulee kokea arvon vaihtaminen oikeudenmukaiseksi eli kokea saamansa arvo riittäväksi tekemiinsä panostuksiin nähden. Tätä teoreettista viitekehystä hyödynnettiin tässä tutkimuksessa selvitettäessä, miten digitaalisen murroksen aikoina liiketoimintamallien innovointia (BMI) voitaisiin parantaa IoT:llä varustetuissa alustaekosysteemeissä. Siten toimijoiden pysyvyyttä voitaisiin parantaa ja verkostojen ulkoisvaikutuksia lisätä.
Tämän tutkimuksen tulokset lisäävät teoreettista ymmärrystä arvon vaihtamisesta IoT:tä hyödyntävien alustaekosysteemien kontekstissa. Tutkimuksessa tunnistettiin sosiaalisen arvon dimensiolle kaksi erilaista tulkintaa. Tutkimuksen perusteella voidaan myös todeta, etteivät teoriat – saati käytännön tekijät – huomioi ehdollista arvoa alustakontekstissa.
Tutkimus tunnisti monitieteellisesti IoT:n ja alustaekosysteemien liiketoimintamalli-innovaatioiden luomiseen tarvittavat osat. Lisäksi tutkimuksessa luotiin uusi malli BMI:lle, joka yhdistää kaksi uutta työkalua eli ekosysteemin arvotaseen ja alustakanvaasin. Käyttämällä mallia iteratiivisesti strategisena työkaluna luodaan arvokasta näkemystä ekosysteemin toimijoille, minkä avulla he voivat luoda yhdessä yhteisen arvolupauksen ja mahdollistaa positiiviset verkostovaikutukset.
Lisäksi tämä tutkimus edistää tutkimusmenetelmiä esittämällä uuden tavan tarkentaa konseptien ominaisuuksia kirjallisuuskatsauksen avulla. Parannettu menetelmä on yhdistelmä lumipallomenetelmää, Porter sanarunkohaku-algoritmia ja temaattista analyysiä. Näitä hyödyntämällä voidaan luoda kattava ja strukturoitu synteesi oleellisesta kirjallisuudesta ja edistää monivivahteisempaa ja syvempää ymmärrystä tutkimusaiheesta. Menetelmää voidaan hyödyntää myös muilla tutkimusalueilla täsmällisten kirjallisuuskatsausten tekemiseen.
Tämä tutkimus avaa väylän arvolupausten arvioinnin tutkimiseen IoT:llä varustetuissa alustaekosysteemeissä. Lisää tutkimusta kuitenkin tarvitaan ennen kuin liiketoimintamahdollisuudet realisoituvat odotetusti. Ehdotettua mallia tulee tutkia vielä useammilla ja pidempikestoisilla tapaustutkimuksilla. Lisäksi monialainen tutkimus voisi tunnistaa yhtäläisyyksiä ja eroavaisuuksia IoT:llä varustettujen alustaekosysteemien haasteissa ja mahdollisuuksissa. Lisäksi tulisi tutkia, miten uudet ja tulevat teknologiat vaikuttavat arvolupauksen muodostamiseen ja arvon tuottamiseen. Tämän tutkimuksen tuloksissa korostetaan ekosysteemissä toimimisen vaatimaa kulttuurimuutosta. Perinteisesti yritykset ovat keskittyneet oman voittonsa maksimoimiseen, mutta ekosysteemeissä tulisi keskittyä koko ekosysteemin kokonaisarvon maksimoimiseen. Tämän kulttuurimuutoksen tarvetta ja sitä, miten muutos voitaisiin saada aikaan, tulisi tutkia lisää.
Yhteenvetona voidaankin todeta, että tämä tutkimus edistää niin liiketoimintamallien innovoinnin teoriaa kuin käytäntöjäkin IoT:llä varustetuissa alustaekosysteemeissä. Se tarjoaa BMI-mallin, joka rakentuu ekosysteemin arvotaseen ympärille. Se mahdollistaa ketterän mallin, jolla IoT:llä varustetun alustaekosysteemin toimijat voivat iteratiivisesti luoda ja kehittää arvolupaustaan. Tämä tutkimus myös kirkastaa käsittelemiään konsepteja ja tarjoaa tuoreen lähestymistavan kirjallisuuskatsauksen tekemiseen. Tämä tutkimus voi auttaa yrityksiä ja yhteisöjä ymmärtämään liiketoimintamallien innovoinnin merkityksen ja näin johtaa ne luomaan kestävämpiä ja kannattavampia ekosysteemejä.Digital transformation is challenging businesses and societies to offer innovative services to customers and to increase profitability through the development of new business models. The Internet of Things (IoT) has been identified as a potent enabler for novel services and businesses. However, despite the potential benefits, successful implementation of IoT-enabled platform ecosystems remains scarce. Research on IoT has mainly focused on technological advancements, while the importance of business model innovation has been largely overlooked. The research in the field of IoT has predominantly focused on technological advancements, disregarding the critical aspect of business model innovation. However, successful implementation of technology largely relies on a well-defined business model that delivers outstanding value propositions.
Social Exchange Theory (SET) is a theoretical framework that is pertinent in the context of IoT-enabled platform ecosystems. According to SET, actors in value exchange should find the distribution of value equitable vis-à-vis the effort invested in value creation. Therefore, in the present research, SET is adopted as a conceptual framework to explore how ecosystem-level business model innovation (BMI) in IoT-enabled platform ecosystems could be enhanced to increase actor retention, and to internalize network externalities to increase the positive network effects during times of digital transformation.
The contribution of this research extends beyond the theoretical development of value exchange in the context of IoT-enabled platform ecosystems. This research identifies two different views of social value and recognizes that in the ecosystem context, conditional value is often overlooked in theoretical discussion and neglected by practitioners.
This research also contributes to BMI theories in the IoT-enabled platform ecosystem context by identifying, in an interdisciplinary manner, the required building blocks, i.e., characteristics of a platform ecosystem BMI, and IoT. Further, a model for BMI is created, which combines two novel frameworks, namely, the Ecosystem Value Balance and the Platform Canvas. This provides ecosystem actors with valuable insights to co-create a joint value proposition and enable positive network effects by utilizing the model iteratively as a strategic tool.
In addition, this research advances research methodologies by presenting a novel approach to clarifying concepts through literature reviews. The method involves a combination of snowballing, Porter stemming, and thematic analysis, which enables a comprehensive and structured synthesis of relevant literature and promotes a more nuanced and deeper understanding of the research topic. This approach can be applied in other research fields, too, to achieve more rigorous and accurate literature reviews.
Although this research opens up avenues for researching value proposition evaluation in IoT-enabled ecosystems, more attention to the business opportunities that can be realized is necessary. The proposed model needs validation with more and longer-term cases, and a cross-industry study could explore potential similarities and differences in the challenges and opportunities of IoT platform ecosystems. Moreover, further research is required to validate the proposed model, explore potential similarities and differences in IoT platform ecosystems, and investigate the role of emerging technologies in shaping the value proposition and value creation processes. Further, the research emphasizes the need for cultural change in companies operating in ecosystems, as traditionally companies have focused on maximizing their profits instead of maximizing the overall value for the whole ecosystem.
In conclusion, this research contributes to the theory and practice of business model innovation in IoT-enabled platform ecosystems by offering a BMI model which relies on value balance in ecosystem contexts and proposes a model for IoT platform ecosystem actors to co-create joint value propositions. It also clarifies related concepts and offers a novel approach to literature reviews. This research can help businesses and societies to understand the importance of business model innovation and to create a more sustainable and profitable ecosystem
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