A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

Visual and interactive exploration of point data

Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine scales of resolution. For instance, socio-economic attributes are commonly assigned to UPC. Hence, they can be represented as points and observable at the postcode level. Using UPC as a common field allows the concatenation of variables from disparate data sources that can potentially support sophisticated spatial analysis. However, visualising UPC in urban areas has at least three limitations. First, at small scales UPC occurrences can be very dense making their visualisation as points difficult. On the other hand, patterns in the associated attribute values are often hardly recognisable at large scales. Secondly, UPC can be used as a common field to allow the concatenation of highly multivariate data sets with an associated postcode. Finally, socio-economic variables assigned to UPC (such as the ones used here) can be non-Normal in their distributions as a result of a large presence of zero values and high variances which constrain their analysis using traditional statistics. This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system developed to visually explore point data. Various well-known visualisation techniques were implemented to enable their interactive and dynamic interrogation. PVT provides multiple representations of point data to facilitate the understanding of the relations between attributes or variables as well as their spatial characteristics. Brushing between alternative views is used to link several representations of a single attribute, as well as to simultaneously explore more than one variable. PVT’s functionality shows how the use of visual techniques embedded in an interactive environment enable the exploration of large amounts of multivariate point data

Adoption of Free Open Source Geographic Information System Solution for Health Sector in Zanzibar Tanzania

\ud The study aims at developing in-depth understanding on how Open Source Geographic Information System technology is used to provide solutions for data visualization in the health sector of Zanzibar, Tanzania. The study focuses on implementing the health visualization solutions for the purpose of bridging the gap during the transition period from proprietary software to the Free Open-Source Software using Key Indicator Data System. The developed tool facilitates data integration between the two District Health Information Software versions and hence served as a gateway solution during the transition process. Implementation challenges that include outdated spatial data and the reluctance of the key users in coping with the new Geographical Information System technologies were also identified. Participatory action research and interviews were used in understanding the requirements for the new tool to facilitate the smooth system development for better health service delivery.\u

ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning

The advent of compact, handheld devices has given us a pool of tracked movement data that could be used to infer trends and patterns that can be made to use. With this flooding of various trajectory data of animals, humans, vehicles, etc., the idea of ANALYTiC originated, using active learning to infer semantic annotations from the trajectories by learning from sets of labeled data. This study explores the application of dimensionality reduction and decision boundaries in combination with the already present active learning, highlighting patterns and clusters in data. We test these features with three different trajectory datasets with objective of exploiting the the already labeled data and enhance their interpretability. Our experimental analysis exemplifies the potential of these combined methodologies in improving the efficiency and accuracy of trajectory labeling. This study serves as a stepping-stone towards the broader integration of machine learning and visual methods in context of movement data analysis.Comment: Bachelor's thesi

DimLift: Interactive Hierarchical Data Exploration through Dimensional Bundling

The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift , a novel visual analysis method for creating and interacting with dimensional bundles . Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.acceptedVersio

Multidimensional Tool for the Visualization of Spatiotemporal Variance in Soil Moisture

With water a precious resource, it is important to understand factors affecting soil moisture. Current research focuses on understanding this relationship; unfortunately these methods are specialized in their applications or overwhelm the user with information making correlations difficult to comprehend. Often, numerical results provide understanding of prominent correlations but miss subtle relationships, hindering subsequent decisions. This project aims to develop a decision making tool combining numerical analysis with visualization techniques to provide the user with the information to analyze soil moisture’s spatial and temporal variability. Current work has shown that self-organizing maps are effective for displaying comprehendible relationships to the user

Structure Segmentation and Transfer Faults in the Marcellus Shale, Clearfield County, Pennsylvania: Implications for Gas Recovery Efficiency and Risk Assessment Using 3D Seismic Attribute Analysis

The Marcellus Shale has become an important unconventional gas reservoir in the oil and gas industry. Fractures within this organic-rich black shale serve as an important component of porosity and permeability useful in enhancing production. Horizontal drilling is the primary approach for extracting hydrocarbons in the Marcellus Shale. Typically, wells are drilled perpendicular to natural fractures in an attempt to intersect fractures for effective hydraulic stimulation. If the fractures are contained within the shale, then hydraulic fracturing can enhance permeability by further breaking the already weakened rock. However, natural fractures can affect hydraulic stimulations by absorbing and/or redirecting the energy away from the wellbore, causing a decreased efficiency in gas recovery, as has been the case for the Clearfield County, Pennsylvania study area. Estimating appropriate distances away from faults and fractures, which may limit hydrocarbon recovery, is essential to reducing the risk of injection fluid migration along these faults. In an attempt to mitigate the negative influences of natural fractures on hydrocarbon extraction within the Marcellus Shale, fractures were analyzed through the aid of both traditional and advanced seismic attributes including variance, curvature, ant tracking, and waveform model regression. Through the integration of well log interpretations and seismic data, a detailed assessment of structural discontinuities that may decrease the recovery efficiency of hydrocarbons was conducted. High-quality 3D seismic data in Central Pennsylvania show regional folds and thrusts above the major detachment interval of the Salina Salt. In addition to the regional detachment folds and thrusts, cross-regional, northwest-trending lineaments were mapped. These lineaments may pose a threat to hydrocarbon productivity and recovery efficiency due to faults and fractures acting as paths of least resistance for induced hydraulic stimulation fluids. These lineaments may represent major transfer faults that serve as pathways for hydraulic fluid migration. Detection and evaluation of fracture orientation and intensity and emphasis on the relationship between fracture intensity and production potential is of high interest in the study area as it entails significant time and cost implications for both conventional and unconventional hydrocarbon exploration and production

Harvesting Intelligence: A Comprehensive Study on Transforming Aquaponic Agriculture with AI and IoT

Aquaponics, an agricultural technique that merges aquaculture and hydroponics, is on the brink of a transformative advancement with the amalgamation of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT). The incorporation of these cutting edge technologies in the field of aquaponics is bringing about a profound transformation in the realm of sustainable agriculture. This extensive investigation delves into the profound influence of these cutting-edge technologies on aquaponics, with a focus on predictive analysis, system optimization, environmental monitoring, and disease prevention. By means of ML and DL algorithms, historical and real-time data are scrutinized in order to forecast environmental fluctuations, optimize resource allocation, and facilitate the growth of crops and fish. IoT devices consistently gather data pertaining to crucial parameters, thereby enabling real-time monitoring and control of the aquaponic system. Furthermore, IoT technology enhances resource utilization and grants the ability to remotely monitor and manage the system. The detection of abnormalities in fish behavior and plant health through the utilization of ML and DL algorithms allows for the implementation of proactive measures aimed at preventing outbreaks and minimizing losses. Furthermore, these advanced technologies also offer personalized recommendations for effective management of various crop and fish species. The incorporation of ML, DL, and IoT into the field of aquaponics signifies a substantial advancement towards a more sustainable, efficient, and productive form of agriculture. These innovative technologies possess the capability to effectively address the challenges associated with global food security by optimizing the utilization of resources, maintaining environmental equilibrium, and mitigating the occurrence of disease outbreaks. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of smart control units in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy. In the context of the examined research endeavors presented in this article, it is anticipated that the utilization of ML, DL and IoT in conjunction with the aquaponics system will yield greater profitability, increased intelligence, enhanced precision, and heightened efficacy