Towards a flexible service integration through separation of business rules

Driven by dynamic market demands, enterprises are continuously exploring collaborations with others to add value to their services and seize new market opportunities. Achieving enterprise collaboration is facilitated by Enterprise Application Integration and Business-to-Business approaches that employ architectural paradigms like Service Oriented Architecture and incorporate technological advancements in networking and computing. However, flexibility remains a major challenge related to enterprise collaboration. How can changes in demands and opportunities be reflected in collaboration solutions with minimum time and effort and with maximum reuse of existing applications? This paper proposes an approach towards a more flexible integration of enterprise applications in the context of service mediation. We achieve this by combining goal-based, model-driven and serviceoriented approaches. In particular, we pay special attention to the separation of business rules from the business process of the integration solution. Specifying the requirements as goal models, we separate those parts which are more likely to evolve over time in terms of business rules. These business rules are then made executable by exposing them as Web services and incorporating them into the design of the business process.\ud Thus, should the business rules change, the business process remains unaffected. Finally, this paper also provides an evaluation of the flexibility of our solution in relation to the current work in business process flexibility research

Can customer sentiment impact firm value? An integrated text mining approach

Developing measures to capture customer sentiment and securing a positive customer experience is a strategic necessity to improve firm profitability and shareholder value. The paper considers the relationship between customer satisfaction, earnings, and firm value as these drives change in stock prices, customer, and investor sentiment. The present study investigates the impact of customer sentiment polarity on stock prices based on Indian automobile sector databased such as the Indian Nifty Auto SNE (Maruti Suzuki, Tata Motors, and Eicher). A top-down approach is adopted to construct a financial proxy-based sentiment index completed with sentiment extracted from automobile news and customer reviews. The paper uses a text mining approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices. The study was initially performed at the overall individual stock from the Nifty Auto NSE but focused on the top three passenger vehicle manufacturing companies i.e., Maruti Suzuki, Tata Motors, and Eicher. It was found that the sentiment index was augmented with news and customer reviews allows predicting more accurately NIFTY AUTO stock price movements. This implies that customer sentiment is a major driver of investor sentiment which in turn impacts the stock market and the firm value. Thus, the present study is an integrated approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices.WOS:00071138140002

Appearance of Corporate Innovation in Financial Reports : A Text-Based Analysis

Innovations are important drivers of economic growth and firm profitability. Firms need funding to generate profitable innovations, which is why it is important to reliably distinguish innovative firms. Innovation indicators are used to measure this innovativeness, and consequently, it is important that the used indicator is reliable and measures innovation as desired. Patents, research and development expenditure and innovation surveys are examples of popular innovation indicators in research literature. However, these indicators have weaknesses, which is why new innovation indicators have been developed. This thesis studies the text-based innovation indicator developed by Bellstam et al. (2019) with a new type of data. Bellstam et al. (2019) created a new text-based innovation indicator that compares corporations’ analyst reports with an innovation textbook as the basis for the indicator. The similarity between these texts created the measurement for innovativeness. Analyst reports are usu-ally subject to charge. However, the 10-K reports used as data for this study are publicly available, and their functionality as the basis of the innovation indicator would mean good availability for the indicator. The study begins by training a Latent Dirichlet allocation (LDA) model with a sample of 10-K documents from 2008-2018. LDA-model is an unsupervised machine learning method, it finds topics in the text documents based on the probabilities of different words. The LDA-model was trained to find 15 topic allocations in the data and the output of the model is the distribution of these topics for each document. The same topic distributions were also allocated for eight samples from innovation textbooks. When the topic distributions were allocated, a Kullback-Leibler-divergence (KL-divergence) was calculated between each text sample and 10-K document. Thus, the KL-divergence calculated is the lowest for those reports that are the most similar to the innovation text and works as the text-based innovation indicator. Finally, the text-based innovation indicator was validated with regression analysis, in other words, it was confirmed that the indicator measures innovation. The text-based indicator was compared with research and development costs and the balance sheet value of brands and patents in different linear regressions. Out of the eight innovation measurements, most had a statistically significant correlation with one or both of the other innovation indicators. The ability of the text-based indicator to predict the development of sales in the next year was studied with regression analysis as well and all of the measurements had a significant effect on this. The most significant findings of this thesis are the relationship of the text-based innovation indicator and other indicators and its ability to predict firms’ sales.Innovaatiot ovat tärkeitä talouskasvun ja yritysten kannattavuuden ajureita. Tuottavien innovaatioiden syntymiseksi yritykset tarvitsevat rahoitusta, minkä takia onkin tärkeää, että innovatiiviset yritykset pystytään tunnistamaan luotettavasti. Innovaatioindikaattoreita käytetään tähän innovatiivisuuden mittaamiseen ja on siksi tärkeää, että käytetty indikaattori on luotettava ja mittaa innovatiivisuutta oikealla tavalla. Kirjallisuudessa paljon käytettyjä innovaatioindikaattoreita ovat esimerkiksi patentit, tutkimus- ja kehitysmenot sekä innovaatiokyselyt. Näissä indikaattoreissa on kuitenkin myös heikkouksia, joiden takia uusia indikaattoreita on alettu kehittää. Tässä tutkielmassa tutkitaan Bellstamin ja muiden (2019) luomaa tekstipohjaista innovaatioindikaattoria erilaisella datalla. Bellstam ja muut (2019) loivat uuden innovaatioindikaattorin, jonka pohjana oli yritysten ana-lyytikkoraporttien vertailu innovaatio-oppikirjan tekstin kanssa, näiden samankaltaisuusver-tailusta saatiin innovaatiomittari. Analyytikkoraportit ovat usein maksullisia. Tässä tutkimuk-sessa aineistona on käytetty lakisääteisiä tilinpäätösraportteja, jotka ovat julkisia tiedostoja, joten niiden toimivuus innovaatioindikaattorin pohjana tarkoittaisi hyvää saatavuutta indi-kaattorille. Tutkimus alkaa Latent Dirichlet allocation (LDA) –mallin harjoittamisella Yhdysvaltalaisten yritysten 10-K, eli tilinpäätösraporteilla vuosilta 2008-2018. LDA-malli on valvomaton koneoppimismenetelmä, eli se etsii datasta itse aihepiirejä sanojen todennäköisyyksien perusteella. LDA-malli asetettiin etsimään datasta 15 eri aihepiiriä raporteissa käytettyjen aiheiden perusteella ja mallin tuloksena on näiden aihepiirien jakautuminen jokaisessa dokumentissa. Samat aihepiirijakaumat haettiin myös kahdeksalle tekstiotokselle innovaatio-oppikirjoista. Aihepiirijakaumien ollessa valmiit, laskettiin Kullback-Leibler-divergenssi (KL-divergenssi) tilinpäätösraporttien ja innovaatio-oppikirjojen tekstiotosten aihepiirijakaumien välille. Laskettu KL-divergenssi on siten matalin niille tilinpäätösraporteille, joiden teksti on lähimpänä kunkin innovaatio-oppikirjan tekstiä ja toimii tekstipohjaisena innovaatioindikaattorina. Lopuksi indikaattorin toimivuus vahvistetaan regressioanalyysillä, eli tutkitaan, että se mittaa innovatiivisuuta. Regressioanalyysillä tutkitaan innovaatiomittarien yhteyttä yritysten tutkimus- ja kehitystoiminnan kuluihin sekä patenttien ja brändien tasearvoon. Kahdeksasta innovaatiomittarista suurimmalla osalla oli tilastollisesti merkitsevä yhteys muuttujista toiseen tai molempiin. Myös uuden innovaatiomittarin kykyä ennustaa yritysten seuraavan vuoden myyntiä tutkittiin regressioanalyysillä ja jokaisella mittarilla oli tilastollisesti merkitsevä yhteys yritysten liikevaihdon muutokseen. Tutkimuksen merkittävin löydös oli tekstipohjaisen innovaatiomittarin yhteys muihin innovaatiomittareihin ja yritysten liikevaihdon kehitykseen

An investigation into design thinking behaviours in early stage radical innovation

The early stage of radical innovation is characterised by uncertainty, data overload and often high rates of change. Schumpeter’s ‘creative destruction’ view of innovation is now exacerbated by ‘hypercompetition’ (D'Aveni, 1999), a theory that describers the increasing rate and intensity of change in modern markets. In the design and strategy literature, design thinking is often positioned as an appropriate mediator of radical innovation in these circumstances, by facilitating interpretation of market uncertainties and moderating organisational behaviours. At its inception radical innovation is determined largely by the cognitive behaviour of the actors involved, often semi-consciously. In this study we set out to distinguish design thinking from analytical thinking and investigate the suitability of both for the effective early stage formation of radical innovation concepts. Additionally, whereas design thinking literature mostly investigates and reports on the benefits of its application, we seek to understand where design thinking’s limitations lie and where it may be better replaced by other forms of cognition. This paper reports at an interim stage of a continuing study. It provides a comprehensive review of relevant literature and a qualitative exploration of two successful innovating SME firms. A framework is given for a novel experimental protocol that will be used in the next stage of the larger study

Fighting Poverty, Profitably: Transforming the Economics of Payments to Build Sustainable, Inclusive Financial Systems

The Gates Foundation's Financial Services for the Poor program (FSP) believes that effective financial services are paramount in the fight against poverty. Nonetheless, today more than 2 billion people live outside the formal financial sector. Increasing their access to high quality, affordable financial services will accelerate the well-being of households, communities, and economies in the developing world. One of the most promising ways to deliver these financial services to the poor -- profitably and at scale -- is by using digital payment platforms.These are the conclusions we have reached as the result of extensive research in pursuit of one of the Foundation's primary missions: to give the world's poorest people the chance to lift themselves out of hunger and extreme poverty.FSP conducted this research because we believe that there is a gap in the fact base and understanding of how payment systems can extend digital services to low income consumers in developing markets. This is a complex topic, with fragmented information and a high degree of country-by-country variability. A complete view across the entire payment system has been missing, limiting how system providers, policy makers, and regulators (groups we refer to collectively as financial inclusion stakeholders) evaluate decisions and take actions. With a holistic view of the payment system, we believe that interventions can have higher impact, and stakeholders can better understand and address the ripple effects that changes to one part of the system can have. In this report, we focus on the economics of payment systems to understand how they can be transformed to serve poor people in a way that is profitable and sustainable in aggregate

Machine and deep learning applications for improving the measurement of key indicators for financial institutions: stock market volatility and general insurance reserving risk

Esta tesis trata de lograr mejoras en los modelos de estimación de los riesgo financieros y actuariales a través del uso de técnicas punteras en el campo del aprendizaje automático y profundo (machine y deep learning), de manera que los modelos de riesgo generen resultados que den un mejor soporte al proceso de toma de decisiones de las instituciones financieras. Para ello, se fijan dos objetivos. En primer lugar, traer al campo financiero y actuarial los mecanismos más punteros del campo del aprendizaje automático y profundo. Los algoritmos más novedosos de este campo son de amplia aplicación en robótica, conducción autónoma o reconocimiento facial, entre otros. En segundo lugar, se busca aprovechar la gran capacidad predictiva de los algoritmos anteriormente adaptados para construir modelos de riesgo más precisos y que, por tanto, sean capaces de generar resultados que puedan dar un mejor soporte a la toma de decisiones de las instituciones financieras. Dentro del universo de modelos de riesgos financieros, esta tesis se centra en los modelos de riesgo de renta variable y reservas de siniestros. Esta tesis introduce dos modelos de riesgo de renta variable y otros dos de reservas. Por lo que se refiere a la renta variable, el primero de los modelos apila algoritmos tales como redes neuronales, bosques aleatorios o regresiones aditivas múltiples con árboles con el objetivo de mejorar la estimación de la volatilidad y, por tanto, generar modelos de riesgo más precisos. El segundo de los modelos de riesgo adapta al mundo financiero y actuarial los Transformer, un tipo de red neuronal que, debido a su alta precisión, ha apartado al resto de algoritmos en el campo del procesamiento del lenguaje natural. Adicionalmente, se propone una extensión de esta arquitectura, llamada Multi-Transformer y cuyo objetivo es mejorar el rendimiento del algoritmo inicial mediante el ensamblaje y aleatorización de los mecanismos de atención. En lo relativo a los dos modelos de reservas introducidos por esta tesis el primero de ellos trata de mejorar la estimación de reservas y generar modelos de riesgo más precisos apilando algoritmos de aprendizaje automático con modelos de reservas basados en estadística bayesiana y Chain Ladder. El segundo modelo de reservas trata de mejorar los resultados de un modelo de uso habitual, como es el modelo de Mack, a través de la aplicación de redes neuronales recurrentes y conexiones residuales

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

On security and privacy of consensus-based protocols in blockchain and smart grid

In recent times, distributed consensus protocols have received widespread attention in the area of blockchain and smart grid. Consensus algorithms aim to solve an agreement problem among a set of nodes in a distributed environment. Participants in a blockchain use consensus algorithms to agree on data blocks containing an ordered set of transactions. Similarly, agents in the smart grid employ consensus to agree on specific values (e.g., energy output, market-clearing price, control parameters) in distributed energy management protocols. This thesis focuses on the security and privacy aspects of a few popular consensus-based protocols in blockchain and smart grid. In the blockchain area, we analyze the consensus protocol of one of the most popular payment systems: Ripple. We show how the parameters chosen by the Ripple designers do not prevent the occurrence of forks in the system. Furthermore, we provide the conditions to prevent any fork in the Ripple network. In the smart grid area, we discuss the privacy issues in the Economic Dispatch (ED) optimization problem and some of its recent solutions using distributed consensus-based approaches. We analyze two state of the art consensus-based ED protocols from Yang et al. (2013) and Binetti et al. (2014). We show how these protocols leak private information about the participants. We propose privacy-preserving versions of these consensus-based ED protocols. In some cases, we also improve upon the communication cost