dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 3 The importance of business intelligence strategy When a company decides to take advantage of BI and use it for its own supply chain, it is important to set up a supply chain business intelligence strategy. Having a reliable strategy is essential for every business to succeed, the same holds true for the implementation of BI in a supply chain. As reported by Sangari and Razmi (2014), the supply chain BI competence is seen as a multidimensional construct competence. The company has to build a full strategy, including three competences: the managerial, technical and cultural competence. The managerial competence aims to relay the right information to the right people at the right time  (Bose, 2009). The technical competence represents the tools and the technologies (like data warehousing) used to gather information in a supply chain in order to make business decisions.
Today, companies use BI in order to collect data quickly, efficiently and to make it available immediately. It provides decision-making support to professionals through reports and dashboards to monitor both analytical and forward-looking business activities. BI collects data from ERP (enterprise resource planning), TMS, and CRM that it then stores in the data warehouse as a central data repository or in data marts via ETL (extraction, transformation and loading) processes which are responsible for retrieving data from all existing operational sources and loading them to the decision-making system. Then BI distributes this data and finally analyses them through data mining, OLAP charts and reporting. As seen above, BI is made up of several components. The following are the major components: a data warehouse is a database dedicated to the storage of all the data used in decision making and decision analysis.
The data warehouse is exclusively reserved for this purpose. Data marts are a smaller version of the data warehouse. They focus on a topic, a theme or a job. In OLAP, within an OLAP database, the data is stored according to a principle of dimensions closely corresponding to the user's search axes, its structure can be seen as a "cube". "OLAP provides multidimensional, summarized views of business data and is used for reporting, analysis, modelling and planning for optimizing the business"  (Sahay and Ranjan, 2008). Data mining is able to find original structures and informal correlations between data. It allows us to better understand the links between apparently distinct phenomena and to anticipate trends that are not yet discernible. As a consequence, BI is a part of business, it allows the company to make decisions clearly and quickly.
The faster the stores send information about what customers buy, the faster the information can be passed on to manufacturers and designers, the faster the supply chain can react and contributes to the optimization of supply chains which are the issue in the search for competitive advantages. Stock reduction and optimization of the supply chain cannot be conceived without good information management. Beyond the traditional operational systems that automate processes, it is without doubt necessary to rely on an appropriate decision-making system like BI. The latter must be based on a data warehouse that integrates all internal and external logistics data and provides all stakeholders with the historical, operational, forecasting or simulation visions they need. Consequently, BI in management of SCM contributes to the differentiation of a business entity.
Real-time business intelligence BI is important in order to make appropriate decisions. As part of this, the concept of realtime BI is starting to attract companies' attention. Real-time BI consists of reducing time and collect instantaneous data. It not only supports the traditional strategic functions of data warehousing, but also provides tactical real-time support for generating corporate actions to respond immediately to events as they occur. Manh et al. (2005)  propose an "event-driven IT infrastructure to leverage BI applications that enable real-time analysis in all business-tobusiness processes, notify actionable recommendations, or trigger business operations automatically, and allow to effectively bridge the gap between BI systems and business processes. " For example, if a company sells clothes online then the company's web site and the company's call centre representatives must have the same updated information on inventory levels, so that, if a customer makes an order and the size or colour is sold out, the customer can be notified and redirected to another similar item.
Real time business intelligence in the supply chain Traditional BI systems are used by several varied sectors like manufacturers, airlines telecommunication providers, retailers, financial services, health systems and hotels and consist of a back-end database, a front-enduser interface, software that processes the information to produce the BI itself, and a reporting system. It produces for these companies customer support, market research, segmenting, product profitability, statistical analysis, inventory and distribution analysis. However, BI requires a complex technology usable only to technical specialists. Moreover, BI takes a long time to yield correct analyses and companies want this analysis in real time for short-term projects. Traditional BI cannot do this and in consequence real time BI is seen as a rescue.
Real time BI detects early situations for planning and coordination of logistics such as delay of freight, stocks alert, and failure of delivery. Real time BI reacts in near real time to changes in the business environment. It analyses data minute-tominute in various time zones and helps firms move to what is called as "zero latency" or real time enterprise. According to  Hackerthorn (2003) , a business is operating with three latency periods: data latency, analysis latency and decision latency. The aim is to reduce the latency to the minimum to be more efficient and this is one of the purposes of real-time BI. Sahay and Ranjan (2008)  noticed that it means "delivering information in a range from milliseconds to a few seconds after the business event". Take the example of Flixbus, a bus is stuck in the traffic jam, the real time BI automatically discovers the problem, analyses it before a decision is needed.
They carried out the seamless inventory to improve its performance and they build the "reserve in store" on the e-commerce website. Moreover, all of the functions of the company are affected by BI, even the support functions such as human resources or financial management. For example, as for the human resources, reports can analyse the movement and the performance of staff, tools can measure the need of the workforce  (Rao P. and Swarup, 2001). HR managers are able to know which employees are efficient, they also see how many people they need to hire for each new project or implementation. Concerning financial management, budgets can be analysed and financial viability can be assessed (profitability per kilometer of distance covered or labour cost, for example) through financial report or data warehousing.
To prove that a company can be more competitive with data analysis, the example of a baseball player will be used. Lewis (2003)  performed research on a baseball team and used data-based analysis on one of the players' performance. It turned the club into a very competitive team. Lidl is one of the many firms that used BI in their processes. They used the software SAP (an ERP software) to analyse a large amount of data, to understand and react to the customer behavior. This allowed them to have a better understanding of the customer and to target the right market. Targeting the right market also means they will have better chance to improve the success of the company. All of the elements stated previously are participating together towards the common and final goal, customer satisfaction. Through profitability analysis of the services offered to the clients, firms are able to know what to offer to each customer.
Introduction The Blockchain technology attracted a lot attention recently [LCO + 16]. Blockchain is a distributed database, managed by a peer-to-peer network that stores a list of blocks or records. Ethereum  [Eth14] , and BitCoin  [Nak09]  are examples of blockchain technologies. Blockchains can be used for many applications such as cryptocurrency, digital wallets, adhoc networks, and remote transactions [LCO + 16, HL16, LMH16, Dzi15, Eth14, Nak09]. One notable application of blockchain is the execution of smart contracts [LCO + 16]. [Copyright notice will appear here once 'preprint' option is removed. ] Smart contracts are what embedded procedures are for databases: programs executed in the blockchain to manage and transfer digital assets. When used in platforms like Ethereum, the contract language is Turing-complete [BDLF + 16].
For example, we have Solidity compilers coded in C/C++ and NodeJs, thirdparty parsers and grammar specifications (JavaScript and ANTLR), plugins for IDEs and editors (IntelliJ, Visual Studio, Vim, Atom, and etc. ). Such tool integration support developers of smart contracts. However, as far as we know, there is a lack of tools for Pharo Smalltalk to handle Solidity smart contracts. Moreover, most academic work towards smart contracts focuses on security [LCO + 16, BDLF + 16, DAK + 15] and not in tool support. In this paper, we plan to partially tackle this lack of tools problem by building a Solidity parser that runs on Pharo Smalltalk. We claim that with a parser and its generated AST (Abstract Syntax Tree), we will be able to develop strong tool support for Solidity contracts. For instance, it would be much easier to create code inspection tools on top of a functional parser than to rely on the purely textual content of the contract.
To accomplish these goals, we used SmaCC (Smalltalk Compiler-Compiler)  [BLGD17]  relying on the Solidity grammar specification to build our parser. However, the Solidity grammar shows many irregularities and ambiguities that we had to tackle before creating a functional parser on SmaCC. Our process to adapt the grammar could aid other developers when dealing with similar grammatic problems. The remainder of the paper is organized as follows. In Section 2, we present some basic concepts on parser generators and the Solidity language. Section 3 describes the parser creation and the process we used to convert the Solidity grammar into a more regular form. In Section 4, we discuss some of the parser limitations and characteristics. Section 5 describes the threats to validity for this research.
The original YACC was developed in C for Unix systems, but now we have implementations of it on several other languages. ANTLR  [Mil05]  differs from the previous tools because it is a top-down parser generator. ANTLR creates predictive LL(k) parsers. By using a variable number of "k" lookahead symbols, ANTLR can handle grammar irregularities better than LR(1) and LL(1) parsers (at the cost of performance). There are implementations of ANTLR for languages such as Java, C++, C#, JavaScript, Python, and others. However, there is no implementation of ANTLR for Pharo. Finally, PetitParser  [BCDL13]  is a scannerless parser generator that relies on parsing expression grammars. PetitParser is integrated with Moose, which facilitates its use on Pharo. The main problem with PetitParser is it does not read a grammar specification, and we must write the grammatic expressions using the Pe-titParser language.
Grammar Changes In this paper, we described grammar changes and adaptations to create a Solidity parser using SmaCC. Even tough, the resulting grammar is not exactly the same as the original, our main goal was to achieve an equivalent grammar that can describe the same Solidity language. We claim that our parser recognizes the same language based on the following reasons: (i) the limitations acknowledged in our parser are also present in the official Solidity compiler, which indicates that is the current accepted behavior for contracts written in Solidity; and (ii) we successfully parsed 117 verified contracts that were deployed in a real blockchain platform.
Solidity Smart Contracts Most research related to Solidity smart contracts leans towards security. For instance, Luu at al. [LCO + 16] try to makes Ethereum based contracts smarter and more secure. First the authors analyze possible exploits that someone can use to take advantage, and then they propose solutions to lower the vulnerabilities in the platform. They also build a tool written in Python, called Oyente, that flags potential security risks in smart contracts. Their tool analyze contracts on the EVM byte code instead of Solidity, because according to them most contracts does not have their Solidity source publicly available. The authors evaluate their tool using 19K contracts on a quantitative and qualitative level. Their tool was successful in detecting security risks in 8K of the evaluated contracts.
Is hierarchy necessary? Richard M. Burton GitHub began without hierarchy; now, it has a traditional hierarchy. Why? Can an organization thrive or even survive without hierarchy? If so, under what conditions or contingencies can it survive? Is hierarchy necessary? Hierarchical organizations are not new: Moses took his thousands to hundreds on down to tens; the Roman Army had its centurions which were aggregated up to legions; the Roman Catholic Church is hierarchical and is long lived. In contrast, we do not have many examples of longed lived non-hierarchical organizations. In this short essay, I proceed from hierarchy to no hierarchy-the reverse of GitHub. The GitHub zoo story is told the other way around-no hierarchy to hierarchy. Here, we will begin with hierarchy and see what might happen if we remove the hierarchy from the organization.
Now, GitHub's motto "In Collaboration we Trust" is a statement of value, who we are, and how we will proceed. Put together, there are many rules in the Weberian sense which define GitHub. Why hierarchy: the contingencies Do we need hierarchy? There are good reasons for hierarchy which suggests that contingencies are important. Weber (1948) : "The development of the money economy, in so far as a pecuriary compensation of the officials is concerned, is a presupposition of bureaucracy", p. 204 "…precision, unity, strict subordination, reduction of friction and of material and personal coststhese are raised to the optimum point in the strictly bureaucratic administration, and especially in its monocratic form. As compared with all collegiate, honorific, and avocational forms of administration, trained bureaucracy is superior on all these points".
Phanish Puranam In a Rashomon-like fashion, I want to offer three different accounts of what might have happened at GitHub. Of course, this plurality of accounts partly reflects our lack of access to an insider's narrative, but since the objective of the Zoo is to stimulate discussion rather than anoint a particular answer as the "truth," I see this as a fruitful exercise. The first version is what we may call the story of "the Inevitability of Authority. " Any organization to exist must have solved four fundamental problems: task division, task allocation, coordination, and motivation  (Puranam et al. 2014). Authority plays a very important role in solving each of these four problems in organizations of even modest size. A founder/boss decides what tasks need to be done, who does them, how people are compensated for doing them, and how different people performing different tasks coordinate their actions (and steps to resolve issues around these when necessary).
Endnotes 1 The company raised 100 million dollars in July 2012 from Andreessen Horowitz. 2 The article is based on secondary and publically available sources. We did approach the company for commentary, but have not received it at the time of going to press. 3 https://github. com/holman/ama/issues/135 4 https://zachholman. com/posts/how-github-works/ 5 https://www. fastcompany. com/3020181/inside-githubs-super-lean-managementstrategy-and-how-it-drives-innovation 6 https://www. fastcompany. com/3020181/inside-githubs-super-lean-managementstrategy-and-how-it-drives-innovation 7 https://github. com/blog/1269 8 https://www. infoworld. com/article/2608907/application-development/github-s-newceo-we-re-serious-about-the-enterprise. html 9 https://techcrunch. com/2015/06/04/github-expands-to-japan-its-first-office-outsidethe-u-s/ 10 https://www.
Introduction The Electronic Health Records for Clinical Research (EHR4CR) technological platform has been developed to enable the trustworthy reuse of hospital electronic health records data for clinical research. The EHR4CR platform can enhance and speed up clinical research scenarios: protocol feasibility assessment, patient identification for recruitment in clinical trials, and clinical data exchange, including for reporting serious adverse events. Our objective was to seed a multi-stakeholder ecosystem to enable the scalable exploitation of the EHR4CR platform in Europe, and to assess its economic sustainability.
By defining the organisational framework that can optimise full business potential, business models also assist funding and capital investment decisions. The objective of the Electronic Health Records for Clinical Research (EHR4CR ) (http://www. ehr4cr. eu/) was to develop a scalable pan-European platform during a 5-year European research project  (2011) (2012) (2013) (2014) (2015)  funded by the European Commission and by the European Federation of Pharmaceutical Industries and Associations (EFPIA), in the frame of the Innovative Medicines Initiative Joint Undertaking Programme (IMI-JU)  [21]. More specifically, this project has developed adaptable, reusable and scalable tools and services for reusing data from electronic health records (EHR) systems for clinical research purposes. With a budget of over 16 million Euros, the EHR4CR consortium involved 34 academic and private partners (10 pharmaceutical companies), and included 11 hospital sites in France, Germany, Poland, Switzerland and the United Kingdom.
It is to date one of the largest of the IMI public-private partnership in this area  [21]. Importantly, the EHR4CR platform has been specially designed and engineered to unlock the contribution of health data from hospital EHR systems, in compliance with the ethical, regulatory and data protection policies and requirements of each participating country, to enable three clinical research scenarios (S): Scenario 1 (S1): Improving protocol feasibility assessments, Scenario 2 (S2): Enhancing and speeding up patient identification for recruitment in clinical trials, and Scenario 3 (S3): Optimising clinical trial data exchange, including for the reporting of severe adverse events (SAE)  [21]. Detailed descriptions of the EHR4CR platform and services have been published  [21, 22]. The EHR4CR platform supports distributed querying to enhance and speed up clinical trial feasibility assessments, patient identification for recruitment, and clinical study execution.
The platform has been piloted at several large academic hospital sites across Europe to demonstrate its functionality, and the effectiveness and security of its tools and services. For this purpose, interfaces between the EHR systems and the central EHR4CR platform have been established. Semantic mapping between local terminologies and the central EHR4CR terminology has been conducted. Clinical data warehouses, compliant with the EHR4CR platform and the associated extract-transform-load processes have been designed and tested. An inventory of data elements corresponding to the most frequently occurring eligibility criteria has been defined. Approval of all data processing steps was gained in accordance with local ethical and legal regulations at each site. In particular, as the pilot sites were active in clinical research, they were able to provide exemplary local governance requirements to complement the ethical inputs referred to above.
g. academic centres, information technology, EHR and electronic data capture (EDC) system vendors, contract research organisations (CROs), clinical research sites, patient advocacy, hospitals, health agencies, pharmaceutical industry, etc. ]. The e-survey was conducted by email invitation in two successive waves, the first one from June-July 2011 (with EHR4CR partners), and the second, from July-September 2011 (with external stakeholders). For this purpose, the e-survey was disseminated by consortium partners through their contact networks to stakeholders across Europe, with a request for these recipients to further invite other colleagues. Rather like the distribution of questionnaires through large mailing list databases, non-response due to a lack of interest or willingness or lack of time could not be distinguished.
Business model simulation In order to assess the economic sustainability of the business model, the number of commercially sponsored and non-commercially sponsored Phase II, III and IV CTs to be conducted in Europe over the next 5 years was calculated. Multiple data sources were investigated. At the time of model development, the BMI-TF selected the EMA EudraCT 2012  [42]  and its retrospective dataset (2007-2011) as baseline values. The worldwide average per-patient costs (USD) per Phase II, III and IV CTs across therapeutic areas under current practices were used as proxy (Table  3 ). The estimated per-patient costs were obtained from the Cutting Edge database These percentages were defined as minimum and maximum values which were then computed and applied to the yearly estimated number of 2012 CTs in order to derive 5year cumulative forecasted ranges (Table  4 ).
The EudraCT 2014 database statistics were later compared with the projected values, confirming the accuracy of the BMI-TF estimations. For the purpose of this study, the BMI-TF defined Phase IV CTs as well-controlled post-registration clinical trials, targeting special populations, or comparing a new intervention with current treatments (standard of care). Hence, Phase IV CTs excluded non-interventional observational studies for assessing comparative effectiveness in real-word contexts. Given an increasing demand for real-world evidence, it is however expected that studies conducted in real-world contexts would also benefit from the EHR4CR platform in the future. The expenses and revenues estimated for an EHR4CR service provider were calculated for each category based on the assumptions defined by the BMI-TF.
As indicated in Using simulation modelling, the potential financial outcomes were estimated per service provider for exploiting the EHR4CR platform. The business simulation calculated the potential revenues assuming that the market uptake objectives set for each EHR4CR scenario would be achieved (Table  6 ). The potential revenues for exploiting the EHR4CR platform were estimated allowing service bundles between EHR4CR scenarios within the ranges of the market uptake objectives set for each, so that EHR4CR scenarios could potentially be used individually, in combination, or sequentially for a given CT (non-mutually exclusive). All potential values were screened by carrying out probabilistic sensitivity analyses according to specific distribution shapes. Using a random number generator programmed in D-Script language, 10'000 Monte Carlo simulations were performed on uniform or normal distribution shapes, depending on the type of data collected.