Social network data analysis for event detection

Cities concentrate enough Social Network (SN) activity to empower rich models. We present an approach to event discovery based on the information provided by three SN, minimizing the data properties used to maximize the total amount of usable data. We build a model of the normal city behavior which we use to detect abnormal situations (events). After collecting half a year of data we show examples of the events detected and introduce some applications.Peer ReviewedPostprint (published version

A new analytics model for large scale multidimensional data visualization

© Springer International Publishing Switzerland 2015. With The Rise Of Big Data, The Challenge For Modern Multidimen-Sional Data Analysis And Visualization Is How It Grows Very Quickly In Size And Complexity. In This Paper, We First Present A Classification Method Called The 5ws Dimensions Which Classifies Multidimensional Data Into The 5ws Definitions. The 5ws Dimensions Can Be Applied To Multiple Datasets Such As Text Datasets, Audio Datasets And Video Datasets. Second, We Establish A Pair-Density Model To Analyze The Data Patterns To Compare The Multidimensional Data On The 5ws Patterns. Third, We Created Two Additional Parallel Axes By Using Pair-Density For Visualization. The Attributes Has Been Shrunk To Reduce Data Over-Crowding In Pair-Density Parallel Coordinates. This Has Achieved More Than 80% Clutter Reduction Without The Loss Of Information. The Experiment Shows That Our Model Can Be Efficiently Used For Big Data Analysis And Visualization

The role of visualisations in social media monitoring systems

Social-Media streams are constantly supplying vast volumes of real-time User Generated Content through platforms such as Twitter, Facebook, and Instagram, which makes it a challenge to monitor and understand. Understanding social conversations has now become a major interest for businesses, PR and advertising agencies, as well as law enforcement and government bodies. Monitoring of social-media allows us to observe large numbers of spontaneous, real-time interactions and varied expression of opinion, often fleeting and private. However, human, expert monitoring is generally unfeasible due to the high volumes of data. This has been a major reason for recent research and development work looking at automated social-media monitoring systems. Such systems often keep the human "out of the loop" as an NLP (Natural Language Processing) pipeline and other data-mining algorithms deal with analysing and extracting features and meaning from the data. This is plagued by a variety of problems, mostly due to the heterogenic, inconsistent and context-poor nature of social-media data, where as a result the accuracy and efficacy of such systems suffers. Nevertheless, automated social-media monitoring systems provide for a scalable, streamlined and often efficient way of dealing with big-data streams. The integration of processing outputs from automated systems and feedback to human experts is a challenge and deserves to be addressed in research literature. This paper will establish the role of the human in the social-media monitoring loop, based on prior systems work in this area. The focus of our investigation will be on use of visualisations for effective feedback to human experts. A specific, custom built system’s case-study in a social-media monitoring scenario will be considered and suggestions on how to bring back the human “into the loop” will be provided. Also some related ethical questions will be briefly considered. It is hoped that this work will inform and provide valuable insight to help improve development of automated social-media monitoring systems

Leveraging Tiled Display for Big Data Visualization Using D3.js

Data visualization has proven effective at detecting patterns and drawing inferences from raw data by transforming it into visual representations. As data grows large, visualizing it faces two major challenges: 1) limited resolution i.e. a screen is limited to a few million pixels but the data can have a billion data points, and 2) computational load i.e. processing of this data becomes computationally challenging for a single node system. This work addresses both of these issues for efficient big data visualization. In the developed system, a High Pixel Density and Large Format display was used enabling the display of fine details on the screen when visualizing data. Apache Spark and Hadoop used in the system allow the computation to be done on a cluster. The system is demonstrated using a global wind flow simulation. The Global Surface Summary of the Day dataset is processed and visualized using web browsers with Data-Driven Documents (D3).js code. We conducted both a performance evaluation and a user study to measure the performance and effectiveness of the system. It was seen that the system was most efficient when visualizing data using streamed bitmap images rather than streamed raw data. The system only rendered images at 6-10 Frames Per Second (FPS) and did not meet our target of rendering images at 30 FPS. The results of the user study concluded that the system is effective and easy to use for data visualization. The outcome of our experiment suggests that the current state of Google Chrome may not be as powerful as required to perform heavy 2D data visualization on the web and still needs more development for visualizing data of large magnitude

Sisälogistiikan hyötyjen kehittäminen varastonhallintajärjestelmän avulla

Tutkimus tehtiin toimeksiantona Leanwarelle, joka on sisälogistiikan toimija ohjelmistoalalla. Leanware kehittää ja toimittaa muun muassa varastonhallintajärjestelmää (WMS), joka on tämän työn keskiössä. Tutkimuksen aihe muodostettiin sen kirjoittajan ja Leanwaren toiveiden mukaisesti. Työn tavoitteena oli löytää ja avata Leanwaren asiakkaiden toimintatapoja, varaston mittaamista sekä muuta hiljaista tietoa, joka ei ole Leanwaren tiedossa. Tämä tutkimus ei sen aiheen laajuudesta johtuen keskity vain logistiikan ympärille sitoutuvaan teoriaan, vaan ottaa huomioon myös taloudellisen näkökulman. Näiden välille muodostetaan aiheeseen sopiva kokonaisuus, joka vastaa Leanwaren tilannetta sisälogistiikan ohjelmistoyrityksenä ja asiakasrajapinnassa. Tutkimus tehtiin tutkimalla teoriaa eri näkökulmista, jonka jälkeen alettiin suunnittelemaan ja toteuttamaan asiakashaastatteluja. Yrityksen sisäistä dialogia käytiin läpi tutkimuksen teon. Näiden perusteella toteutettiin prototyyppi, joka esiteltiin Leanwarella sisäisesti. Haastattelujen tuloksena todettiin, ettei asiakkailla ole olemassa yhtenäistä rahalliseen arvoon perustuvaa mittaustapaa, vaikka toimintaa mitattiinkin monin eri tavoin. Asiakkaat arvostivat eri asioita riippuen pitkälti yrityksen omista tavoitteista ja tilanteesta. Kehitelty prototyyppi pyrkii puuttumaan yhtenäisen mittariston puutteeseen keräämällä dataa, jonka perusteella esitetään ja ennustetaan rahallisia hyötyjä visuaalisessa muodossa. Ne auttavat asiakasta tunnistamaan ongelmia ja kehityskohteita sekä kohdentamaan resursseja. Tavoitteena on esittää mahdollisimman läpinäkyvästi rahalliset hyödyt ja niiden kehittyminen. Tutkimuksen perusteella Leanware pystyy jatkokehittämään esiteltyä prototyyppiä ja tutkimuksen laatija pystyy olemaan mukana kehityksessä. Tavoitteena on pitkällä aikavälillä muodostaa toimiva palvelu ja työkalu Leanwaren ja sen asiakkaiden käyttöön

Text in Visualization: Extending the Visualization Design Space

This thesis is a systematic exploration and expansion of the design space of data visualization specifically with regards to text. A critical analysis of text in data visualizations reveals gaps in existing frameworks and the use of text in practice. A cross-disciplinary review across fields such as typography, cartography and technical applications yields typographic techniques to encode data into text and provides the scope for the expanded design space. Mapping new attributes, techniques and considerations back to well understood visualization principles organizes the design space of text in visualization. This design space includes: 1) text as a primary data type literally encoded into alphanumeric glyphs, 2) typographic attributes, such as bold and italic, capable of encoding additional data onto literal text, 3) scope of mark, ranging from individual glyphs, syllables and words; to sentences, paragraphs and documents, and 4) layout of these text elements applicable most known visualization techniques and text specific techniques such as tables. This is the primary contribution of this thesis (Part A and B). Then, this design space is used to facilitate the design, implementation and evaluation of new types of visualization techniques, ranging from enhancements of existing techniques, such as, extending scatterplots and graphs with literal marks, stem & leaf plots with multivariate glyphs and broader scope, and microtext line charts; to new visualization techniques, such as, multivariate typographic thematic maps; text formatted to facilitate skimming; and proportionally encoding quantitative values in running text – all of which are new contributions to the field (Part C). Finally, a broad evaluation across the framework and the sample visualizations with cross-discipline expert critiques and a metrics based approach reveals some concerns and many opportunities pointing towards a breadth of future research work now possible with this new framework. (Part D and E)