Exploiting Time Series Analysis in Twitter to Measure a Campaign Process Performance

© 2017 IEEE. While there are several metrics to measure business process performance, recently there is an additional requirement from businesses to evaluate business processes based on their impact on users. In this work, we evaluate business process performance using social media analytics. We view a marketing campaign as a business process and we evaluate its performance based on its impact on the Twitter. We propose a new way to calculate the \u27follow\u27 relationship in Twitter based on the users\u27 reaction to the marketing campaign process activities and we use time series and sentiment analysis for defining and measuring performance. We re-build the Twitter graph based on users\u27 reactions to the marketing activities in time and we are using community detection algorithms to identify the size of the \u27follow\u27 community and thus we define metrics to calculate the impact of the marketing/campaign process. We evaluate our approach using a dataset for a given politician. We re-construct the campaign process as a set of activities on specific topics (promotions) in time using LDA. Our results show that social media analytics can be used as a valid metric for assessing business processes performance

Code Quality Evaluation Methodology Using The ISO/IEC 9126 Standard

This work proposes a methodology for source code quality and static behaviour evaluation of a software system, based on the standard ISO/IEC-9126. It uses elements automatically derived from source code enhanced with expert knowledge in the form of quality characteristic rankings, allowing software engineers to assign weights to source code attributes. It is flexible in terms of the set of metrics and source code attributes employed, even in terms of the ISO/IEC-9126 characteristics to be assessed. We applied the methodology to two case studies, involving five open source and one proprietary system. Results demonstrated that the methodology can capture software quality trends and express expert perceptions concerning system quality in a quantitative and systematic manner.Comment: 20 pages, 14 figure

Discovering Influential Twitter Authors Via Clustering And Ranking On Apache Storm

Nowadays several millions of people are throughout the day active, while hundreds of new accounts are created daily on social media. Thousands of short-length posts or tweets are posted on Twitter, a popular micro-blogging platform by a vast variety of authors and thus creating a widely diverse social content. The emerged diversity not only does indicate a remarkable strength, but also reveals a certain kind of difficulty when attempting to find Twitter’s authoritative and influencing authors. This work introduces a two-step algorithmic approach for discovering these authors. A set of metrics and features are, firstly, extracted from the social network e.g. friends and followers and the content of the tweets written by the author are extracted. Then, Twitter’s most authoritative authors are discovered by employing two distinct approaches, one which relies on probabilistic while the other applies fuzzy clustering. In particular, the former, initially, employs the Gaussian Mixture Model to identify the most authoritative authors and then introduces a novel ranking technique which relies on computing the cumulative Gaussian distribution of the extracted metrics and features. On the other hand, the latter combines the Gaussian Mixture Model with fuzzy c-means and subsequently the derived authors are ranked via the Borda count technique. The results indicate that the second scheme was able to find more authoritative authors in the benchmark dataset. Both approaches were designed, implemented, and executed on a local cluster of the Apache Storm framework, a cloud-based platform which supports streaming data and real-time scenarios

An Experimental Study of Uncertainty in Coordination Games

Global games and Poisson games have been proposed to address equilibrium indeterminacy in Common Knowledge Coordination games. The present study investigates in a controlled setup, using as controls Common Knowledge games, whether idiosyncratic uncertainty about economic fundamentals (Global games) or uncertainty about the number of actual players (Poisson games) may influence subjects' behavior. We find that uncertainty about the number of actual players has more influence on subjects' behavior than idiosyncratic uncertainty about economic fundamentals. Furthermore, subjects' behavior under Poisson population?size uncertainty is closer to the respective theoretical prediction than subjects' behavior under idiosyncratic uncertainty about economic fundamentals

THE IMPACT OF CLIMATE CHANGE ON THE STORM SURGES OF THE GREEK SEAS

Διερευνάται η επίδραση της κλιματικής αλλαγής στην εξέλιξη των μετεωρολογικών παλιρροιών στο Αιγαίο και το Ιόνιο Πέλαγος και την παράκτια ζώνη της Ελλάδας. Η ανάλυση καλύπτει την περίοδο 1951-2100 (κλιματικό σενάριο Α1Β για τoν 21ο αιώνα) και στηρίζεται σε προσομοιώσεις με ένα διδιάστατο υδροδυναμικό μοντέλο υψηλής χωρικής ανάλυση (GreCSSM). Η σύγκριση των προσομοιωμένων αποτελεσμάτων έναντι επί τόπου παρατηρήσεων με βάση στατιστικά μέτρα/δείκτες είναι ικανοποιητική. Παρουσιάζονται επίσης οι μελλοντικές τάσεις, η μεταβλητότητα και η συχνότητα εμφάνισης έντονων γεγονότων μετεωρολογικής παλίρροιας στις θαλάσσιες λεκάνες που βρέχουν την ελληνικές παράκτιες περιοχές. Δίνονται επίσης εκτιμήσεις για το σήμα της πιθανής κλιματικής αλλαγής μέχρι το 2100, μαζί τις μέσες, ετήσιες και υπερετήσιες ακραίες τιμές της θυελλογενούς ανύψωσης της θαλάσσιας στάθμης στο Αιγαίο και το Ιόνιο Πέλαγος.The impact of climate change on the evolution of storm surges in the Aegean and Ionian Seas (AIS) and the Greek coastal zone is studied. The analysis covers the 1951-2100 period (Α1Β climate scenario for the 21st century), and hinges on simulations with a two-dimensional hydrodynamic model of high spatial resolution (GreCSSM). The comparison of model results against in situ observations is satisfactory based on statistical measures and indices. Future trends, variability and occurrence frequency of severe storm surge events are also presented concerning the Greek maritime basins and coastal regions. Estimations are also provided for the probable climate change signal until 2100, along with averaged, intra- and inter-annual maxima of storm-induced sea level height of the Aegean and Ionian Seas

A case of bilateral self-induced keratoconus in a patient with tourette syndrome associated with compulsive eye rubbing: case report

<p>Abstract</p> <p>Background</p> <p>Tourette syndrome is a neurologic disorder that is characterized by repetitive muscle contractions that produce stereotyped movements or sounds. Approximately 50% of individuals with TS also exhibit obsessive-compulsive behaviors including eye rubbing. We report a case of bilateral self-induced keratoconus in a patient with TS, associated with compulsive eye rubbing.</p> <p>Case presentation</p> <p>A 35-year-old man was first seen in our clinic as an outpatient due to rapid deterioration of vision in his right eye associated with pain and tearing, over a period of one month. Slit lamp biomicroscopy of the right eye showed a central stromal scar due to corneal hydrops. Clinical examination and corneal topography of the left eye were normal. Six months later the patient developed corneal hydrops of his left eye. During the following examinations his vision continued to deteriorate in both eyes, while a central stromal scar was forming in his left cornea. Four years after the initial examination the patient's visual acuity was no light perception in the right eye and counting fingers at 33 cm in the left eye. His right eye was phthisic.</p> <p>Conclusions</p> <p>Our patient developed a rapidly progressing bilateral corneal ectasia and phthisis of his right eye during a time period of 4 years. This unusual pattern suggests that the patient's compulsive behavior compromised both of his corneas and led to bilateral keratoconus.</p