Relevant bibliographies by topics / Data interaction analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data interaction analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Data interaction analysis.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Data interaction analysis"

1

Volle, Michel. "Interaction between data analysis and telecommunications." Applied Stochastic Models and Data Analysis 8, no. 1 (March 1992): 57–65. http://dx.doi.org/10.1002/asm.3150080108.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Dias, Tiago Gerheim Souza, and Adam Bezuijen. "Data Analysis of Pile Tunnel Interaction." Journal of Geotechnical and Geoenvironmental Engineering 141, no. 12 (December 2015): 04015051. http://dx.doi.org/10.1061/(asce)gt.1943-5606.0001350.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Fisher, Carolanne, and Penelope Sanderson. "Exploratory sequential data analysis." Interactions 3, no. 2 (March 1996): 25–34. http://dx.doi.org/10.1145/227181.227185.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Richmond, Sally, Orli Schwartz, Katherine A. Johnson, Marc L. Seal, Katherine Bray, Camille Deane, Lisa B. Sheeber, Nicholas B. Allen, and Sarah Whittle. "Exploratory Factor Analysis of Observational Parent–Child Interaction Data." Assessment 27, no. 8 (September 15, 2018): 1758–76. http://dx.doi.org/10.1177/1073191118796557.

Full text
Abstract:
The majority of studies using observational coding systems for family interaction data derive scales describing family members’ behaviors based on rational/theoretical approaches. This study explored an empirical approach to identifying the component structure of parent–child observational data that incorporated the affective context of the interaction. Dyads of 155 typically developing 8-year-olds and their mothers completed questionnaires and two interaction tasks, one each designed to illicit positive and negative interactions. Behaviors were coded based on a modified version of the Family Interaction Macro-coding System. Multiple factor analysis identified four-component solutions for the maternal and child data. For both, two of the components included negative behaviors, one positive behavior, and one communicative behavior. Evidence for the validity of the maternal and child components was demonstrated by associations with child depression and anxiety symptoms and behavioral problems. Preliminary evidence supports an empirical approach to identify context-specific components in parent–child observational data.
APA, Harvard, Vancouver, ISO, and other styles
5

Han, Ying, Liang Cheng, and Weiju Sun. "Analysis of Protein-Protein Interaction Networks through Computational Approaches." Protein & Peptide Letters 27, no. 4 (March 17, 2020): 265–78. http://dx.doi.org/10.2174/0929866526666191105142034.

Full text
Abstract:
The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.
APA, Harvard, Vancouver, ISO, and other styles
6

Arnau, V., S. Mars, and I. Mar n. "Iterative Cluster Analysis of Protein Interaction Data." Bioinformatics 21, no. 3 (September 16, 2004): 364–78. http://dx.doi.org/10.1093/bioinformatics/bti021.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

K., Priya. "Stock Market Interaction with US Dollar Exchange Rates Using Panel Data Analysis: Evidence from BRIC Countries." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (July 20, 2020): 255–64. http://dx.doi.org/10.5373/jardcs/v12i7/20202007.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Jofre, Ana, Steve Szigeti, and Sara Diamond. "Materializing data." DAT Journal 1, no. 2 (December 27, 2016): 2–14. http://dx.doi.org/10.29147/2526-1789.dat.2016v1i2p2-14.

Full text
Abstract:
The visualization of data elucidates trends and patterns in the phenomena that the data represents, and opens accessibility to understanding complicated human and natural processes represented by data sets. Research indicates that interacting with a visualization amplfies cognition and analysis. A single visualization may show only one facet of the data. To examine the data from multiple perspectives, engaged citizens need to be able to construct their own visualizations from a data set. Many tools for data visualization have responded to this need, allowing non-data experts to manipulate and gain insights into their data, but most of these tools are restricted to the computer screen, keyboard, and mouse. Cognition and analysis may be strengthened even more through embodied interaction with data, whether through data sculpture or haptic and tangible interfaces. We present here the rationale for the design of a tool that allows users to probe a data set, through interactions with graspable (tangible) three-dimensional objects, rather than through a keyboard and mouse interaction. We argue that the use of tangibles facilitates understanding abstract concepts, and facilitates many concrete learning scenarios. Another advantage of using tangibles over screen-based tools is that they foster collaboration, which can promote a productive working and learning environment.
APA, Harvard, Vancouver, ISO, and other styles
9

Emamjomeh, Abbasali, Darush Choobineh, Behzad Hajieghrari, Nafiseh MahdiNezhad, and Amir Khodavirdipour. "DNA–protein interaction: identification, prediction and data analysis." Molecular Biology Reports 46, no. 3 (March 26, 2019): 3571–96. http://dx.doi.org/10.1007/s11033-019-04763-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Maher, Carmel, Mark Hadfield, Maggie Hutchings, and Adam de Eyto. "Ensuring Rigor in Qualitative Data Analysis." International Journal of Qualitative Methods 17, no. 1 (July 10, 2018): 160940691878636. http://dx.doi.org/10.1177/1609406918786362.

Full text
Abstract:
Deep and insightful interactions with the data are a prerequisite for qualitative data interpretation, in particular, in the generation of grounded theory. The researcher must also employ imaginative insight as they attempt to make sense of the data and generate understanding and theory. Design research is also dependent upon the researchers’ creative interpretation of the data. To support the research process, designers surround themselves with data, both as a source of empirical information and inspiration to trigger imaginative insights. Constant interaction with the data is integral to design research methodology. This article explores a design researchers approach to qualitative data analysis, in particular, the use of traditional tools such as colored pens, paper, and sticky notes with the CAQDAS software, NVivo for analysis, and the associated implications for rigor. A design researchers’ approach which is grounded in a practice which maximizes researcher data interaction in a variety of learning modalities ensures the analysis process is rigorous and productive. Reflection on the authors’ research analysis process, combined with consultation with the literature, would suggest digital analysis software packages such as NVivo do not fully scaffold the analysis process. They do, however, provide excellent data management and retrieval facilities that support analysis and write-up. This research finds that coding using traditional tools such as colored pens, paper, and sticky notes supporting data analysis combined with digital software packages such as NVivo supporting data management offer a valid and tested analysis method for grounded theory generation. Insights developed from exploring a design researchers approach may benefit researchers from other disciplines engaged in qualitative analysis.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Data interaction analysis"

1

Dowling, Michelle Veronica. "Semantic Interaction for Symmetrical Analysis and Automated Foraging of Documents and Terms." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104682.

Full text
Abstract:
Sensemaking tasks, such as reading many news articles to determine the truthfulness of a given claim, are difficult. These tasks require a series of iterative steps to first forage for relevant information and then synthesize this information into a final hypothesis. To assist with such tasks, visual analytics systems provide interactive visualizations of data to enable faster, more accurate, or more thorough analyses. For example, semantic interaction techniques leverage natural or intuitive interactions, like highlighting text, to automatically update the visualization parameters using machine learning. However, this process of using machine learning based on user interaction is not yet well defined. We begin our research efforts by developing a computational pipeline that models and captures how a system processes semantic interactions. We then expanded this model to denote specifically how each component of the pipeline supports steps of the Sensemaking Process. Additionally, we recognized a cognitive symmetry in how analysts consider data items (like news articles) and their attributes (such as terms that appear within the articles). To support this symmetry, we also modeled how to visualize and interact with data items and their attributes simultaneously. We built a testbed system and conducted a user study to determine which analytic tasks are best supported by such symmetry. Then, we augmented the testbed system to scale up to large data using semantic interaction foraging, a method for automated foraging based on user interaction. This experience enabled our development of design challenges and a corresponding future research agenda centered on semantic interaction foraging. We began investigating this research agenda by conducting a second user study on when to apply semantic interaction foraging to better match the analyst's Sensemaking Process.
Doctor of Philosophy
Sensemaking tasks such as determining the truthfulness of a claim using news articles are complex, requiring a series of steps in which the relevance of each piece of information within the articles is first determined. Relevant pieces of information are then combined together until a conclusion may be reached regarding the truthfulness of the claim. To help with these tasks, interactive visualizations of data can make it easier or faster to find or combine information together. In this research, we focus on leveraging natural or intuitive interactions, such organizing documents in a 2-D space, which the system uses to perform machine learning to automatically adjust the visualization to better support the given task. We first model how systems perform such machine learning based on interaction as well as model how each component of the system supports the user's sensemaking task. Additionally, we developed a model and accompanying testbed system for simultaneously evaluating both data items (like news articles) and their attributes (such as terms within the articles) through symmetrical visualization and interaction methods. With this testbed system, we devised and conducted a user study to determine which types of tasks are supported or hindered by such symmetry. We then combined these models to build an additional testbed system that implemented a searching technique to automatically add previously unseen, relevant pieces of information to the visualization. Using our experience in implementing this automated searching technique, we defined design challenges to guide future implementations, along with a research agenda to refine the technique. We also devised and conducted another user study to determine when such automated searching should be triggered to best support the user's sensemaking task.
APA, Harvard, Vancouver, ISO, and other styles
2

Kuhnke, Dominik [Verfasser]. "Spray/Wall-Interaction Modelling by Dimensionless Data Analysis / Dominik Kuhnke." Aachen : Shaker, 2004. http://d-nb.info/1186574682/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Laha, Bireswar. "Immersive Virtual Reality and 3D Interaction for Volume Data Analysis." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/51817.

Full text
Abstract:
This dissertation provides empirical evidence for the effects of the fidelity of VR system components, and novel 3D interaction techniques for analyzing volume datasets. It provides domain-independent results based on an abstract task taxonomy for visual analysis of scientific datasets. Scientific data generated through various modalities e.g. computed tomography (CT), magnetic resonance imaging (MRI), etc. are in 3D spatial or volumetric format. Scientists from various domains e.g., geophysics, medical biology, etc. use visualizations to analyze data. This dissertation seeks to improve effectiveness of scientific visualizations. Traditional volume data analysis is performed on desktop computers with mouse and keyboard interfaces. Previous research and anecdotal experiences indicate improvements in volume data analysis in systems with very high fidelity of display and interaction (e.g., CAVE) over desktop environments. However, prior results are not generalizable beyond specific hardware platforms, or specific scientific domains and do not look into the effectiveness of 3D interaction techniques. We ran three controlled experiments to study the effects of a few components of VR system fidelity (field of regard, stereo and head tracking) on volume data analysis. We used volume data from paleontology, medical biology and biomechanics. Our results indicate that different components of system fidelity have different effects on the analysis of volume visualizations. One of our experiments provides evidence for validating the concept of Mixed Reality (MR) simulation. Our approach of controlled experimentation with MR simulation provides a methodology to generalize the effects of immersive virtual reality (VR) beyond individual systems. To generalize our (and other researchers') findings across disparate domains, we developed and evaluated a taxonomy of visual analysis tasks with volume visualizations. We report our empirical results tied to this taxonomy. We developed the Volume Cracker (VC) technique for improving the effectiveness of volume visualizations. This is a free-hand gesture-based novel 3D interaction (3DI) technique. We describe the design decisions in the development of the Volume Cracker (with a list of usability criteria), and provide the results from an evaluation study. Based on the results, we further demonstrate the design of a bare-hand version of the VC with the Leap Motion controller device. Our evaluations of the VC show the benefits of using 3DI over standard 2DI techniques. This body of work provides the building blocks for a three-way many-many-many mapping between the sets of VR system fidelity components, interaction techniques and visual analysis tasks with volume visualizations. Such a comprehensive mapping can inform the design of next-generation VR systems to improve the effectiveness of scientific data analysis.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Chen. "From network to pathway: integrative network analysis of genomic data." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77121.

Full text
Abstract:
The advent of various types of high-throughput genomic data has enabled researchers to investigate complex biological systems in a systemic way and started to shed light on the underlying molecular mechanisms in cancers. To analyze huge amounts of genomic data, effective statistical and machine learning tools are clearly needed; more importantly, integrative approaches are especially needed to combine different types of genomic data for a network or pathway view of biological systems. Motivated by such needs, we make efforts in this dissertation to develop integrative framework for pathway analysis. Specifically, we dissect the molecular pathway into two parts: protein-DNA interaction network and protein-protein interaction network. Several novel approaches are proposed to integrate gene expression data with various forms of biological knowledge, such as protein-DNA interaction and protein-protein interaction for reliable molecular network identification. The first part of this dissertation seeks to infer condition-specific transcriptional regulatory network by integrating gene expression data and protein-DNA binding information. Protein-DNA binding information provides initial relationships between transcription factors (TFs) and their target genes, and this information is essential to derive biologically meaningful integrative algorithms. Based on the availability of this information, we discuss the inference task based on two different situations: (a) if protein-DNA binding information of multiple TFs is available: based on the protein-DNA data of multiple TFs, which are derived from sequence analysis between DNA motifs and gene promoter regions, we can construct initial connection matrix and solve the network inference using a constraint least-squares approach named motif-guided network component analysis (mNCA). However, connection matrix usually contains a considerable amount of false positives and false negatives that make inference results questionable. To circumvent this problem, we propose a knowledge based stability analysis (kSA) approach to test the conditional relevance of individual TFs, by checking the discrepancy of multiple estimations of transcription factor activity with respect to different perturbations on the connections. The rationale behind stability analysis is that the consistency of observed gene expression and true network connection shall remain stable after small perturbations are applied to initial connection matrix. With condition-specific TFs prioritized by kSA, we further propose to use multivariate regression to highlight condition-specific target genes. Through simulation studies comparing with several competing methods, we show that the proposed schemes are more sensitive to detect relevant TFs and target genes for network inference purpose. Experimentally, we have applied stability analysis to yeast cell cycle experiment and further to a series of anti-estrogen breast cancer studies. In both experiments not only biologically relevant regulators are highlighted, the condition-specific transcriptional regulatory networks are also constructed, which could provide further insights into the corresponding cellular mechanisms. (b) if only single TF's protein-DNA information is available: this happens when protein-DNA binding relationship of individual TF is measured through experiments. Since original mNCA requires a complete connection matrix to perform estimation, an incomplete knowledge of single TF is not applicable for such approach. Moreover, binding information derived from experiments could still be inconsistent with gene expression levels. To overcome these limitations, we propose a linear extraction scheme called regulatory component analysis (RCA), which can infer underlying regulation relationships, even with partial biological knowledge. Numerical simulations show significant improvement of RCA over other traditional methods to identify target genes, not only in low signal-to-noise-ratio situations and but also when the given biological knowledge is incomplete and inconsistent to data. Furthermore, biological experiments on Escherichia coli regulatory network inferences are performed to fairly compare traditional methods, where the effectiveness and superior performance of RCA are confirmed. The second part of the dissertation moves from protein-DNA interaction network up to protein-protein interaction network, to identify dys-regulated protein sub-networks by integrating gene expression data and protein-protein interaction information. Specifically, we propose a statistically principled method, namely Metropolis random walk on graph (MRWOG), to highlight condition-specific PPI sub-networks in a probabilistic way. The method is based on the Markov chain Monte Carlo (MCMC) theory to generate a series of samples that will eventually converge to some desired equilibrium distribution, and each sample indicates the selection of one particular sub-network during the process of Metropolis random walk. The central idea of MRWOG is built upon that the essentiality of one gene to be included in a sub-network depends on not only its expression but also its topological importance. Contrasted to most existing methods constructing sub-networks in a deterministic way and therefore lacking relevance score for each protein, MRWOG is capable of assessing the importance of each individual protein node in a global way, not only reflecting its individual association with clinical outcome but also indicating its topological role (hub, bridge) to connect other important proteins. Moreover, each protein node is associated with a sampling frequency score, which enables the statistical justification of each individual node and flexible scaling of sub-network results. Based on MRWOG approach, we further propose two strategies: one is bootstrapping used for assessing statistical confidence of detected sub-networks; the other is graphic division to separate a large sub-network to several smaller sub-networks for facilitating interpretations. MRWOG is easy to use with only two parameters need to be adjusted, one is beta value for performing random walk and another is Quantile level for calculating truncated posteriori mean. Through extensive simulations, we show that the proposed scheme is not sensitive to these two parameters in a relatively wide range. We also compare MRWOG with deterministic approaches for identifying sub-network and prioritizing topologically important proteins, in both cases MRWG outperforms existing methods in terms of both precision and recall. By utilizing MRWOG generated node/edge sampling frequency, which is actually posteriori mean of corresponding protein node/interaction edge, we illustrate that condition-specific nodes/interactions can be better prioritized than the schemes based on scores of individual node/interaction. Experimentally, we have applied MRWOG to study yeast knockout experiment for galactose utilization pathways to reveal important components of corresponding biological functions; we also applied MRWSOG to study breast cancer patient prognostics problems, where the sub-network analysis could lead to an understanding of the molecular mechanisms of antiestrogen resistance in breast cancer. Finally, we conclude this dissertation with a summary of the original contributions, and the future work for deepening the theoretical justification of the proposed methods and broadening their potential biological applications such as cancer studies.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
5

Pradhananga, Nipesh. "Construction site safety analysis for human-equipment interaction using spatio-temporal data." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52326.

Full text
Abstract:
The construction industry has consistently suffered the highest number of fatalities among all human involved industries over the years. Safety managers struggle to prevent injuries and fatalities by monitoring at-risk behavior exhibited by workers and equipment operators. Current methods of identifying and reporting potential hazards on site involve periodic manual inspection, which depends upon personal judgment, is prone to human error, and consumes enormous time and resources. This research presents a framework for automatic identification and analysis of potential hazards by analyzing spatio-temporal data from construction resources. The scope of the research is limited to human-equipment interactions in outdoor construction sites involving ground workers and heavy equipment. A grid-based mapping technique is developed to quantify and visualize potentially hazardous regions caused by resource interactions on a construction site. The framework is also implemented to identify resources that are exposed to potential risk based on their interaction with other resources. Cases of proximity and blind spots are considered in order to create a weight-based scoring approach for mapping hazards on site. The framework is extended to perform ``what-if'' safety analysis for operation planning by iterating through multiple resource configurations. The feasibility of using both real and simulated data is explored. A sophisticated data management and operation analysis platform and a cell-based simulation engine are developed to support the process. This framework can be utilized to improve on-site safety awareness, revise construction site layout plans, and evaluate the need for warning or training workers and equipment operators. It can also be used as an education and training tool to assist safety managers in making better, more effective, and safer decisions.
APA, Harvard, Vancouver, ISO, and other styles
6

Asur, Sitaram. "A Framework for the Static and Dynamic Analysis of Interaction Graphs." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243902523.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Florez, Omar Ulises. "Knowledge Extraction in Video Through the Interaction Analysis of Activities Knowledge Extraction in Video Through the Interaction Analysis of Activities." DigitalCommons@USU, 2013. https://digitalcommons.usu.edu/etd/1720.

Full text
Abstract:
Video is a massive amount of data that contains complex interactions between moving objects. The extraction of knowledge from this type of information creates a demand for video analytics systems that uncover statistical relationships between activities and learn the correspondence between content and labels. However, those are open research problems that have high complexity when multiple actors simultaneously perform activities, videos contain noise, and streaming scenarios are considered. The techniques introduced in this dissertation provide a basis for analyzing video. The primary contributions of this research consist of providing new algorithms for the efficient search of activities in video, scene understanding based on interactions between activities, and the predicting of labels for new scenes.
APA, Harvard, Vancouver, ISO, and other styles
8

Alam, Sayeed Safayet. "Analysis of Eye-Tracking Data in Visualization and Data Space." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3473.

Full text
Abstract:
Eye-tracking devices can tell us where on the screen a person is looking. Researchers frequently analyze eye-tracking data manually, by examining every frame of a visual stimulus used in an eye-tracking experiment so as to match 2D screen-coordinates provided by the eye-tracker to related objects and content within the stimulus. Such task requires significant manual effort and is not feasible for analyzing data collected from many users, long experimental sessions, and heavily interactive and dynamic visual stimuli. In this dissertation, we present a novel analysis method. We would instrument visualizations that have open source code, and leverage real-time information about the layout of the rendered visual content, to automatically relate gaze-samples to visual objects drawn on the screen. Since such visual objects are shown in a visualization stand for data, the method would allow us to necessarily detect data that users focus on or Data of Interest (DOI). This dissertation has two contributions. First, we demonstrated the feasibility of collecting DOI data for real life visualization in a reliable way which is not self-evident. Second, we formalized the process of collecting and interpreting DOI data and test whether the automated DOI detection can lead to research workflows, and insights not possible with traditional, manual approaches.
APA, Harvard, Vancouver, ISO, and other styles
9

Cannon, Paul C. "Extending the information partition function : modeling interaction effects in highly multivariate, discrete data /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2263.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wictorin, Sebastian. "Streamlining Data Journalism: Interactive Analysis in a Graph Visualization Environment." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22498.

Full text
Abstract:
This thesis explores the topic of how one can streamline a data journalists analytical workflow in a graph visualization environment. Interactive graph visualizations have been used recently by data journalists to investigate the biggest leaks of data in history. Graph visualizations empower users to find patterns in their connected data, and as the world continuously produces more data, the more important it becomes to make sense of it. The exploration was done by conducting semi-structured interviews with users, which illuminated three categories of insights called Graph Readability, Charts in Graphs and Temporality. Graph Readability was the category that were conceptualized and designed by integrating user research and data visualization best practises. The design process was concluded with a usability test with graph visualization developers, followed by a final iteration of the concept. The outcome resulted in a module that lets users simplify their graph and preserve information by aggregating nodes with similar attributes.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Data interaction analysis"

1

Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Stillwell, John C. H. Technologies for migration and commuting analysis: Spatial interaction data applications. Hershey, PA: Business Science Reference, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Vicenç, Quera, ed. Analyzing interaction: Sequential analysis with SDIS & GSEQ. Cambridge: Cambridge University Press, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Stillwell, John C. H. The development of a web-based interface to census interaction data. Leeds: School of Geography, University of Leeds, 2000.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Transcribing talk and interaction: Issues in the representation of communication data. Amsterdam: John Benjamins Pub. Co., 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Bakeman, Roger. Analyzing interaction: Sequential analysis with SDIS and GSEQ. Cambridge: Cambridge University Press, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Interpreting qualitative data: Methods for analysing talk, text, and interaction. 2nd ed. London: Sage Publications, 2001.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

David, Silverman. Interpreting qualitative data: Methods for analysing talk, text, and interaction. London: Sage Publications, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Desimone, Leslie A. Use of computer programs STLK1 and STWT1 for analysis of stream-aquifer hydraulic interaction. Marlborough, Mass: U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Desimone, Leslie A. Use of computer programs STLK1 and STWT1 for analysis of stream-aquifer hydraulic interaction. Marlborough, Mass: U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Data interaction analysis"

1

Cleophas, Ton J., and Aeilko H. Zwinderman. "Interaction." In Clinical Data Analysis on a Pocket Calculator, 139–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27104-0_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Cleophas, Ton J., and Aeilko H. Zwinderman. "Interaction." In Clinical Data Analysis on a Pocket Calculator, 231–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27104-0_41.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Fischer, Manfred M., and Jinfeng Wang. "Spatial Interaction Models and Spatial Dependence." In Spatial Data Analysis, 61–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21720-3_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Fischer, Manfred M., and Jinfeng Wang. "Models and Methods for Spatial Interaction Data." In Spatial Data Analysis, 47–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21720-3_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Cleophas, Ton J., and Aeilko H. Zwinderman. "Interaction." In Statistical Analysis of Clinical Data on a Pocket Calculator, 49–50. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1211-9_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tobi, Hilde, Paul B. van den Berg, and Lolkje T. W. de Jong-van den Berg. "The InterAction Database: Synergy of Science and Practice in Pharmacy." In Medical Data Analysis, 206–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39949-6_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Fischer, Manfred M., Martin Reismann, and Thomas Scherngell. "Spatial Interaction and Spatial Autocorrelation." In Perspectives on Spatial Data Analysis, 61–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-642-01976-0_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Conversano, Claudio, and Elise Dusseldorp. "Simultaneous Threshold Interaction Detection in Binary Classification." In Data Analysis and Classification, 225–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Klaus, Martin, and Ralf Wagner. "Exploring the Interaction Structure of Weblogs." In Advances in Data Analysis, Data Handling and Business Intelligence, 545–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01044-6_50.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Durand, Jean-François, and Rosaria Lombardo. "Interaction Terms in Non-linear PLS via Additive Spline Transformation." In Between Data Science and Applied Data Analysis, 22–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-18991-3_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Data interaction analysis"

1

Browne, Jeffrey, Bongshin Lee, Sheelagh Carpendale, Nathalie Riche, and Timothy Sherwood. "Data analysis on interactive whiteboards through sketch-based interaction." In the ACM International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2076354.2076383.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tang, Lei, Xufei Wang, and Huan Liu. "Uncoverning Groups via Heterogeneous Interaction Analysis." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Shahir, Hamed Yaghoubi, Uwe Glasser, Amir Yaghoubi Shahir, and Hans Wehn. "Maritime situation analysis framework: Vessel interaction classification and anomaly detection." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363883.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Lee, Seokmin, and Won Woo Ro. "Interaction Data Analysis for Personalized Recommendation System." In 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2020. http://dx.doi.org/10.1109/icce-asia49877.2020.9276772.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Siddharth, P. Doshi, and Shripad Deshpande. "Embedded system design for real-time interaction with Smart Wheelchair." In 2016 Symposium on Colossal Data Analysis and Networking (CDAN). IEEE, 2016. http://dx.doi.org/10.1109/cdan.2016.7570917.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Khoury, Raymes, Tim Dawborn, and Weidong Huang. "Visualising web browsing data for user behaviour analysis." In the 23rd Australian Computer-Human Interaction Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2071536.2071564.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Menaker, Tom. "Data-Driven Analysis of Animal Behavioral Patterns." In ACI'2020: Seventh International Conference on Animal-Computer Interaction. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3446002.3446004.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Alzamora, Patricio, Quang Vinh Nguyen, Simeon Simoff, and Daniel Catchpoole. "A novel 3D interactive visualization for medical data analysis." In the 24th Australian Computer-Human Interaction Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2414536.2414539.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ivanova, Iustina, Marina Andric, Andrea Janes, Francesco Ricci, and Floriano Zini. "Climbing Activity Recognition and Measurement with Sensor Data Analysis." In ICMI '20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3395035.3425303.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Di, and Zhichao Xu. "Bibliometric analysis of the core thesis system of Interaction Design Research on Human-Computer Interaction." In 2020 International Conference on Big Data and Social Sciences (ICBDSS). IEEE, 2020. http://dx.doi.org/10.1109/icbdss51270.2020.00031.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Data interaction analysis"

1

Terrill, Eric J. CBLAST Data Analysis: Air-Sea Interaction Floats. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada495437.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Briscoe, William John, Igor I. Strakovsky, and Ronald L. Workman. A Data Analysis Center for Electromagnetic and Hadronic Interaction. Office of Scientific and Technical Information (OSTI), May 2015. http://dx.doi.org/10.2172/1213477.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Salom, Jaume, Anna Joanna Marszal, José Candanedo, Joakim Widén, Karen Byskov Lindberg, and Igor Sartori. Analysis Of Load Match and Grid Interaction Indicators in NZEB with High-Resolution Data. IEA Solar Heating and Cooling Programme, March 2014. http://dx.doi.org/10.18777/ieashc-task40-2014-0001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Briscoe, William John, Igor I. Strakovsky, and Ronald L. Workman. A Data Analysis Center for Electromagnetic and Hadronic Interaction. Products of the DAC members. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1213415.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tarko, Andrew P., Qiming Guo, and Raul Pineda-Mendez. Using Emerging and Extraordinary Data Sources to Improve Traffic Safety. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317283.

Full text
Abstract:
The current safety management program in Indiana uses a method based on aggregate crash data for conditions averaged over several-year periods with consideration of only major roadway features. This approach does not analyze the risk of crashes potentially affected by time-dependent conditions such as traffic control, operations, weather and their interaction with road geometry. With the rapid development of data collection techniques, time-dependent data have emerged, some of which have become available for safety management. This project investigated the feasibility of using emerging and existing data sources to supplement the current safety management practices in Indiana and performed a comprehensive evaluation of the quality of the new data sources and their relevance to traffic safety analysis. In two case studies, time-dependent data were acquired and integrated to estimate their effects on the hourly probability of crash and its severity on two selected types of roads: (1) rural freeways and (2) signalized intersections. The results indicate a considerable connection between hourly traffic volume, average speeds, and weather conditions on the hourly probability of crash and its severity. Although some roadway geometric features were found to affect safety, the lack of turning volume data at intersections led to some counterintuitive results. Improvements have been identified to be implemented in the next phase of the project to eliminate these undesirable results.
APA, Harvard, Vancouver, ISO, and other styles
6

Ma, Kwan-Liu. Interactive Correlation Analysis and Visualization of Climate Data. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1325752.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Krishnamagaru, Dharmesh, Leana Dusang, Vishnumohan Das, and M. S. Foster. Naval Interactive Data Analysis System (NIDAS), Software User's Manual. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada273847.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bethel, E. Wes, Scott Campbell, Eli Dart, Jason Lee, Steven A. Smith, Kurt Stockinger, Brian Tierney, and Kesheng Wu. Interactive Analysis of Large Network Data Collections UsingQuery-Driven Visualization. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/891627.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

McDonald, J. A., F. O'Sullivan, and W. Stuetzle. Computing environments, interactive graphics and nonparametric methods for data analysis. Office of Scientific and Technical Information (OSTI), March 1992. http://dx.doi.org/10.2172/5159651.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

McDonald, J. A., F. O'Sullivan, and W. Stuetzle. Computing environments, interactive graphics and nonparametric methods for data analysis. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/6600498.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Data interaction analysis' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography