Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

Crowdsourcing as a support to solving complex problems in entrepreneurial settings

1Dottorato di Ricerca in Management (XXVIII ciclo), LUISS Guido Carli, Roma, 2017. Relatori: Prof. Francesco Rullani, Prof. Marion Poetz (Copenhagen Business School).openCrowdsourcing is a newly-developed field that has helped a number of organizations to solved complex problems concerning quantities of information and resource accessibility. Many entrepreneurs have utilized crowdsourcing to their benefit, bypassing traditional forms of fundraising in order to increase their probability of success. Paper 1 will look specifically at the ways in which crowdsourcing can perform such a role, supporting the entrepreneur through each phase of the entrepreneurial process. Paper 2 will expand on this idea by exploring the effects that crowdsourcing can have on a company’s performance. Looking specifically at data provided by AngelList, a popular crowdsourcing platform, we’ll attempt to analyze the benefits that the technology has had on businesses by comparing crowdsourcing-based investment paths to those of traditional investors. Specifically, we measured the performance of both traditional and crowdsourcing-base business ventures over a 2-year period, using data extracted from Mattermark. We aim to shed light, here, on the ability of crowdsourcing to produce better performance in the medium-term. Paper 3 will investigate the effects that crowd size and diversity can have on the performance of a crowdsourced venture. AngelList’s data set will be useful in unpacking the relationship between the volume and diversity of a syndicate’s backers to see how these attributes can be beneficial or detrimental to a firm. While a significant amount of research has been undertaken around this topic, we have found that there are many gaps in the available literature. Where researchers have written extensively about the potential for crowdsourcing to support the discovery, exploitation and execution of entrepreneurial opportunities, much of this literature does not take into account the nature of currently-used crowdsourcing platforms. Throughout each of these papers, we’ll attempt to expand into the territory left unexplored by existing research, paying specific attention to the individual attributes phase of the entrepreneurial model.openDottorato di Ricerca in ManagementBALDELLI, FEDERICOBaldelli, Federic

When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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Economic resilience and crowdsourcing platforms

The increased interdependence and complexity of modern societies have increased the need to involve all members of a community into solving problems. In times of great uncertainty, when communities face threats of different kinds and magnitudes, the traditional top-down approach where government provides solely for community wellbeing is no longer plausible. Crowdsourcing has emerged as an effective means of empowering communities with the potential to engage individuals in innovation, self-organization activities, informal learning, mutual support, and political action that can all lead to resilience. However, there remains limited resource on the topic. In this paper, we outline the various forms of crowdsourcing, economic and community resilience, crowdsourcing and economic resilience, and a case study of the Nepal earthquake. his article presents an exploratory perspective on the link can be found between crowdsourcing and economic resilience. It introduces and describes a framework that can be used to study the impact of crowdsourcing initiatives for economic resilience by future research. An initial a set of indicators to be used to measure the change in the level of resilience is presented.info:eu-repo/semantics/publishedVersio

Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

When Moneyball Meets the Beautiful Game: A Predictive Analytics Approach to Exploring Key Drivers for Soccer Player Valuation

To measure the market value of a professional soccer (i.e., association football) player is of great interest to soccer clubs. Several gaps emerge from the existing soccer transfer market research. Economics literature only tests the underlying hypotheses between a player’s market value or wage and a few economic factors. Finance literature provides very theoretical pricing frameworks. Sports science literature uncovers numerous pertinent attributes and skills but gives limited insights into valuation practice. The overarching research question of this work is: what are the key drivers of player valuation in the soccer transfer market? To lay the theoretical foundations of player valuation, this work synthesizes the literature in market efficiency and equilibrium conditions, pricing theories and risk premium, and sports science. Predictive analytics is the primary methodology in conjunction with open-source data and exploratory analysis. Several machine learning algorithms are evaluated based on the trade-offs between predictive accuracy and model interpretability. XGBoost, the best model for player valuation, yields the lowest RMSE and the highest adjusted R2. SHAP values identify the most important features in the best model both at a collective level and at an individual level. This work shows a handful of fundamental economic and risk factors have more substantial effect on player valuation than a large number of sports science factors. Within sports science factors, general physiological and psychological attributes appear to be more important than soccer-specific skills. Theoretically, this work proposes a conceptual framework for soccer player valuation that unifies sports business research and sports science research. Empirically, the predictive analytics methodology deepens our understanding of the value drivers of soccer players. Practically, this work enhances transparency and interpretability in the valuation process and could be extended into a player recommender framework for talent scouting. In summary, this work has demonstrated that the application of analytics can improve decision-making efficiency in player acquisition and profitability of soccer clubs

Crowdsourcing as a support to solving complex problems in entrepreneurial settings

Crowdsourcing is a newly-developed field that has helped a number of organizations to solved complex problems concerning quantities of information and resource accessibility. Many entrepreneurs have utilized crowdsourcing to their benefit, bypassing traditional forms of fundraising in order to increase their probability of success. Paper 1 will look specifically at the ways in which crowdsourcing can perform such a role, supporting the entrepreneur through each phase of the entrepreneurial process. Paper 2 will expand on this idea by exploring the effects that crowdsourcing can have on a company’s performance. Looking specifically at data provided by AngelList, a popular crowdsourcing platform, we’ll attempt to analyze the benefits that the technology has had on businesses by comparing crowdsourcing-based investment paths to those of traditional investors. Specifically, we measured the performance of both traditional and crowdsourcing-base business ventures over a 2-year period, using data extracted from Mattermark. We aim to shed light, here, on the ability of crowdsourcing to produce better performance in the medium-term. Paper 3 will investigate the effects that crowd size and diversity can have on the performance of a crowdsourced venture. AngelList’s data set will be useful in unpacking the relationship between the volume and diversity of a syndicate’s backers to see how these attributes can be beneficial or detrimental to a firm. While a significant amount of research has been undertaken around this topic, we have found that there are many gaps in the available literature. Where researchers have written extensively about the potential for crowdsourcing to support the discovery, exploitation and execution of entrepreneurial opportunities, much of this literature does not take into account the nature of currently-used crowdsourcing platforms. Throughout each of these papers, we’ll attempt to expand into the territory left unexplored by existing research, paying specific attention to the individual attributes phase of the entrepreneurial model.Crowdsourcing is a newly-developed field that has helped a number of organizations to solved complex problems concerning quantities of information and resource accessibility. Many entrepreneurs have utilized crowdsourcing to their benefit, bypassing traditional forms of fundraising in order to increase their probability of success. Paper 1 will look specifically at the ways in which crowdsourcing can perform such a role, supporting the entrepreneur through each phase of the entrepreneurial process. Paper 2 will expand on this idea by exploring the effects that crowdsourcing can have on a company’s performance. Looking specifically at data provided by AngelList, a popular crowdsourcing platform, we’ll attempt to analyze the benefits that the technology has had on businesses by comparing crowdsourcing-based investment paths to those of traditional investors. Specifically, we measured the performance of both traditional and crowdsourcing-base business ventures over a 2-year period, using data extracted from Mattermark. We aim to shed light, here, on the ability of crowdsourcing to produce better performance in the medium-term. Paper 3 will investigate the effects that crowd size and diversity can have on the performance of a crowdsourced venture. AngelList’s data set will be useful in unpacking the relationship between the volume and diversity of a syndicate’s backers to see how these attributes can be beneficial or detrimental to a firm. While a significant amount of research has been undertaken around this topic, we have found that there are many gaps in the available literature. Where researchers have written extensively about the potential for crowdsourcing to support the discovery, exploitation and execution of entrepreneurial opportunities, much of this literature does not take into account the nature of currently-used crowdsourcing platforms. Throughout each of these papers, we’ll attempt to expand into the territory left unexplored by existing research, paying specific attention to the individual attributes phase of the entrepreneurial model.LUISS PhD Thesi

The Value of Social Media for Predicting Stock Returns - Preconditions, Instruments and Performance Analysis

The cumulative dissertation of Michael Nofer examines whether Social Media platforms can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which consist largely of user generated content. For instance, emotions can be extracted in order to identify the investors' risk appetite and in turn the willingness to invest in stocks. Discussion forums also provide an opportunity to extract opinions on certain stocks. Taking Social Media platforms as examples, the dissertation examines the forecasting quality of user generated content on the Internet