Predictive Analytics in Forecasting

Predicting future demand can be of tremendous help to businesses in scheduling and allocating appropriate amounts of material and labor. The more accurate these predictions are, the more the business will save money by matching supply with demand as closely as possible. The approach for an accurate forecast, and the goal of this project, involves using data analytics techniques on past historical sales data. Working with Campus Dining, a year\u27s worth of their daily sales data will be analyzed and ultimately used for the end result of both an accurate forecasting technique and a way to display the results in a user friendly manner. The feasibility and effectiveness of doing so will be determined at the end of this project

Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.

In healthcare, a tremendous amount of clinical and laboratory tests, imaging, prescription and medication data are being collected. Big data analytics on these data aim at early detection of disease which will help in developing preventive measures and in improving patient care. Parkinson disease is the second-most common neurodegenerative disorder in the United States. To find a cure for Parkinson\u27s disease biological, clinical and behavioral data of different cohorts are collected, managed and propagated through Parkinson’s Progression Markers Initiative (PPMI). Applying big data technology to this data will lead to the identification of the potential biomarkers of Parkinson’s disease. Data collected in human clinical studies is imbalanced, heterogeneous, incongruent and sparse. This study focuses on the ways to overcome the challenges offered by PPMI data which is wide and gappy. This work leverages the initial discoveries made through descriptive studies of various attributes. The exploration of data led to identifying the significant attributes. We are further working to build a software suite that enables end to end analysis of Parkinson’s data (from cleaning and curating data, to imputation, to dimensionality reduction, to multivariate correlation and finally to identify potential biomarkers)

Big data and Parkinson’s: Exploration, analyses, data challenges and visualization

In healthcare, a tremendous amount of clinical, laboratory tests, imaging, prescription and medication data are collected. Big data analytics on these data aim at early detection of disease which will help in developing preventive measures and in improving patient care. Parkinson disease is the second-most common neurodegenerative disorder in the United States. To find a cure for Parkinson\u27s disease biological, clinical and behavioral data of different cohorts are collected, managed and propagated through Parkinson’s Progression Markers Initiative (PPMI). Applying big data technology to this data will lead to the identification of the potential biomarkers of Parkinson’s disease. Data collected in human clinical studies is imbalanced, heterogeneous, incongruent and sparse. This study focuses on the ways to overcome the challenges offered by PPMI data which is wide and incongruent. This work leverages the initial discoveries made through descriptive studies of various attributes. The exploration of data led to identifying the significant attributes. This research project focuses on data munging or data wrangling, creating the structural metadata, curating the data, imputing the missing values, using the emerging big data analysis methods of dimensionality reduction, supervised machine learning on the reduced dimensions dataset, and finally an interactive visualization. The simple interactive visualization platform will abstract the domain expertise from the sophisticated mathematics and will enable a democratization of the exploration process. Visualization build on D3.Js is interactive and will enable manual exploration of traits that correlate with the disease severity

Relational Model Bases: A Technical Approach to Real-time Business Intelligence and Decision Making

This article presents a technical approach to acquiring quality, real-time decision-making information within organizations and illustrates this approach with an extended case study. Using relational model bases for real-time, operational decision making in organizations facilitates a transition to dynamic (vs. forecast-driven) resource allocation decisions. These and related systems offer development of a new generation of DSS applications which can be applied to extend preemptive decision making across many industries. This approach is illustrated through a description of a detailed conceptual case (scenario) pertaining to its application in agribusiness. This approach to decision making can be viewed as an extension of well-known techniques pertaining to DSS but also represents the opportunity to address problems not amenable to traditional post hoc analysis. Researchers can learn from the accumulated knowledge pertaining to DSS but can also examine innovations that push forward into new territories. The article presents and discusses a variety of emergent research questions prompted by the application of these technologies in the business environment

Tele-entomology and tele-parasitology: A citizen science-based approach for surveillance and control of Chagas disease in Venezuela.

Chagas Disease (CD), a chronic infection caused by the Trypanosoma cruzi parasite, is a Neglected Tropical Disease endemic to Latin America. With a re-emergence in Venezuela during the past two decades, the spread of CD has proved susceptible to, and inhibitable by a digital, real-time surveillance system effectuated by Citizen Scientists in communities throughout the country. The #TraeTuChipo (#BringYourKissingBug) campaign implemented in January 2020, has served as such a strategy counting on community engagement to define the current ecological distribution of CD vectors despite the absence of a functional national surveillance program. This pilot campaign collected data through online surveys, social media platforms, and/or telephone text messages. A total of 79 triatomine bugs were reported from eighteen Venezuelan states; 67 bugs were identified as Panstrongylus geniculatus, 1 as Rhodnius pictipes, 1 as Triatoma dimidiata, and 10 as Triatoma maculata. We analyzed 8 triatomine feces samples spotted from 4 Panstrongylus geniculatus which were confirmed positive by qPCR for T. cruzi. Further molecular characterization of discrete typing units (DTUs), revealed that all samples contained TcI, the most highly diverse and broadly distributed strain of T. cruzi. Moreover, analysis of the mitochondrial 12S gene revealed Myotis keaysi, Homo sapiens, and Gallus gallus as the main triatomine feeding sources. This study highlights a novel Citizen Science approach which may help improve the surveillance systems for CD in endemic countries

Mortality in Europe: διαδικτυακή χαρτογραφική εφαρμογή διαχρονικών δεδομένων θνησιμότητας

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Evaluation Of Information Visualization For Decision Making Support In An Emergency Department Information System.

The purpose of this dissertation is to propose an evaluation framework to assess various IV techniques in EDIS and provide recommendations for developers

Personal Profile Monitoring

Employees are the human capital which, to a great extent, contributes to the success and development of high-performance and sustainable organizations. In a work environment, there is a need to provide a tool for tracking and following-up on each employees' professional progress, while staying aligned with the organization’s strategic and operational goals and objectives. The research work within this Thesis aims to contribute to improve employees' selfawareness and auto-regulation; two predominant research areas are also studied and analyzed: Visual Analytics and Gamification. The Visual Analytics enables the specification of personalized dashboard interfaces with alerts and indicators to keep employees aware of their skills and to continuously monitor how to improve their expertise, promoting simultaneously behavioral change and adoption of good-practices. The study of Gamification techniques with Talent Management features enabled the design of new processes to engage, motivate, and retain highly productive employees, and to foster a competitive working environment, where employees are encouraged to be involved in new and rewarding activities, where knowledge and experience are recognized as a relevant asset. The Design Science Research was selected as the research methodology; the creation of new knowledge is therefore based on an iterative cycle addressing concepts such as design, analysis, reflection, and abstraction. By collaborating in an international project (Active@Work), funded by the Active and Assisted Living Programme, the results followed a design thinking approach regarding the specification of the structure and behavior of the Skills Development Module, namely the identification of requirements and the design of an innovative info-structure of metadata to support the user experience. A set of mockups were designed based on the user role and main concerns. Such approach enabled the conceptualization of a solution to proactively assist the management and assessment of skills in a personalized and dynamic way. The outcomes of this Thesis aims to demonstrate the existing articulation between emerging research areas such as Visual Analytics and Gamification, expecting to represent conceptual gains in these two research fields