Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. The circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e., able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of cognitive digital twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. More emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.
This paper addresses a problem of automatic estimation of three journalistic news values, more specifically frequency, threshold and proximity, by applying various text mining methods. Although theoretical frameworks already exist in social sciences that identify if an event is newsworthy, these manual techniques require enormous amount of time and domain knowledge. Thus, we illustrate how text mining can assist journalistic work by finding news values of different international publishers across the world. Our experiments both on a collection of news articles from different publishers about Apple's launch of new iPhone 6 and Apple Watch and on a wider collection of documents confirm that some journalists still follow some of the well-known journalistic values. Furthermore, we acknowledge that news values are often orthodox and outdated, and no longer apply to all publishers. We also outline possible future implications of our approach to work on interaction between text mining and journalistic domains. Povzetek: Članek obravnava problem avtomatske ocene novičarskih vrednosti, natančneje: pogostosti, prag pomembnosti ter bližine (oz. relevance), z uporabo različnih metod avtomatske analize teksta. Čeprav v družboslovju obstaja več teoretičnih ogrodij, ki določajo ali je nek dogodek vreden poročanja, te temeljijo na »ročnem« delu in zahtevajo veliko časa ter globoko poznavanje domene. V članku nakažemo, kako lahko avtomatska analiza teksta pomaga pri novinarskem delu z uvidom v novičarske vrednosti na globalnem nivoju in med velikimi, mednarodnimi mediji. Rezultati naših poskusov na zbirki člankov iz različnih virov (spletnih časopisov) o splovitvi izdelkov iPhone 6 in Apple Watch podjetja Apple potrjujejo, da novinarji še sledijo nekaterim uveljavljenim novičarskim vrednostim. Ob tem ugotavljamo, da so nekatere novičarske vrednosti preveč ortodoksne in zastarele ter ne veljajo več za vse novičarske vire. Na koncu orišemo načrtovano nadaljnje delo in možne implikacije uporabe avtomatskih metod analize teksta na novinarsko delo.
Digital marketing is a domain where data analytics are a key factor to gaining competitive advantage and return of investment for companies running and monetizing digital marketing campaigns on, e.g., search engines and social media. In this paper, we propose an endto-end approach to enrich marketing campaigns performance data with third-party event data (e.g., weather events data) and to analyze the enriched data in order to predict the e↵ect of such events on campaigns' performance, with the final goal of enabling advanced optimization of the impact of digital marketing campaigns. The use of semantic technologies is central to the proposed approach: event data are made available in a format more amenable to enrichment and analytics, and the actual data enrichment technique is based on semantic data reconciliation. The enriched data are represented as Linked Data and managed in a NoSQL database to enable processing of large amounts of data. We report on the development of a pilot to build a weather-aware digital marketing campaign scheduler for JOT Internet Media-a world leading company in the digital marketing domain that has amassed a huge amount of data on campaigns performance over the years-which predicts the best date and region to launch a marketing campaign within a seven-day timespan. Additionally, we discuss benefits and limitations of applying semantic technologies to deliver better optimization strategies and competitive advantage.
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