Principles for information visualization spreadsheets

The visualization spreadsheet provides a framework for exploring large and complex data sets. Structuring user interactions using a spreadsheet paradigm creates a powerful tool for information visualization     

Non-visual navigation of spreadsheets

The problem of non-visual navigation of the information in Excel? spreadsheet is that using current technologies, no overview is available for the user about different components found in the spreadsheet. Several attributes associated with spreadsheets make them not easy to navigate by individuals who are blind. The large amount of information stored in the spreadsheet, the multi-dimensional nature of the contents, and the several features it includes cannot be readily linearized by a screen reader or Braille display (e.g., charts and tables). A user-centered design paradigm is followed to build an accessible system for non-visual navigation of Microsoft Excel? spreadsheet. The proposed system provides the user with a hierarchical overview of the navigated components in an Excel? spreadsheet. The system is multi-modal, and it provides the user with different navigation and reading modes for the non-visual navigation of a spreadsheet. This will help the users in adapting the non-visual navigation according to the task the user needs to accomplish.     

Model provenance tracking and inference for integrated environmental modelling

Integrated environmental modeling (IEM) provides a systematic way to couple models for integrated analysis. Coupled models in IEM often exchange data at runtime for time-step based executions. It is a challenge to track which raw observations or intermediate data exchanged at runtime contribute to individual model outputs. Time-step level provenance is needed to audit the trail of model execution or perform diagnosis in case of anomalies. This paper introduces a method to support provenance awareness in IEM. It suggests that individual models should expose necessary interfaces for provenance capturing in IEM environments. The provenance is represented using the W3C PROV model for interoperability. Fine-grained provenance is inferred based on coarse-grained provenance and temporal characteristics of computations of numerical time marching models. The approach is implemented in OpenMI-compliant models. A case study of model provenance tracking and inference on the watershed runoff simulation scenario illustrates the applicability of the approach.     

Beyond spreadsheets: tools for building decision support systems

The complexity and long development time inherent in building decision support systems has thus far prevented their wide use. A new class of tools, DSS generators, seeks to cut the lead time between development and deployment. DSS generators provide tools that make it easier and faster to develop models, data, and user interfaces that are customized to the application's requirements. Using a DSS generator reduces DSS development to a decision analysis task-which requires expertise in decision analysis and mathematical modeling-rather than a programming task. DSS generators are crucial to the success of DSSs in practice. We describe the state of the art in DSS generator software, specifically in the realm of decision analysis methods. Decision analysis techniques account for the uncertain, dynamic, and multicriteria aspects of decisions. Essentially, they aid the evaluation of alternatives in the face of trade-offs. Well known decision analysis methods include decision trees and influence diagrams. We briefly describe the features of 11 commercially available DSS generators that specialize in decision analysis. Although not a comprehensive, complete analysis of these tools, the article clarifies the idea of DSS generators as DSS development environments and presents an overview of the progress in this area     

Problem solving with spreadsheets*

The recent availability of spreadsheets has provided new opportunities for pupil and student use of microcomputers. Whilst much has been written on the use of database and word processing packages, the educational uses of spreadsheets is relatively undocumented. In this paper reference is made to exploratory work aimed at providing opportunities for additional experience in problem solving.     

Canonical locality preserving Latent Variable Model for discriminative pose inference

Discriminative approaches for human pose estimation model the functional mapping, or conditional distribution, between image features and 3D poses. Learning such multi-modal models in high dimensional spaces, however, is challenging with limited training data; often resulting in over-fitting and poor generalization. To address these issues Latent Variable Models (LVMs) have been introduced. Shared LVMs learn a low dimensional representation of common causes that give rise to both the image features and the 3D pose. Discovering the shared manifold structure can, in itself, however, be challenging. In addition, shared LVM models are often non-parametric, requiring the model representation to be a function of the training set size. We present a parametric framework that addresses these shortcomings. In particular, we jointly learn latent spaces for both image features and 3D poses by maximizing the non-linear dependencies in the projected latent space, while preserving local structure in the original space; we then learn a multi-modal conditional density between these two low-dimensional spaces in the form of Gaussian Mixture Regression. With this model we can address the issue of over-fitting and generalization, since the data is denser in the learned latent space, as well as avoid the need for learning a shared manifold for the data. We quantitatively compare the performance of the proposed method to several state-of-the-art alternatives, and show that our method gives a competitive performance.     

可扩展的电子表格查询外数据源方法

电子表格是一种分析工具,具有友好的用户界面,提供了简单计算模型。但与RDBMS相比,电子表格没有一个共享机制,而导致多个电子表格需要产生多个副本。此外,电子表格也不能提供可扩展的计算。为了解决共享性和可扩展性问题,提出把Excel计算自动地转换为SQL。可以从一个系统中输入数据,在系统中用类似的Excel公式定义计算,然后把它作为一个SQL视图转换和存储起来,然后通过系统执行Excel计算。实验显示,方法是可行和有效的。     

Implicit finite difference techniques for the advection–diffusion equation using spreadsheets

This study proposes one-dimensional advection–diffusion equation (ADE) with finite differences method (FDM) using implicit spreadsheet simulation (ADEISS). By changing only the values of temporal and spatial weighted parameters with ADEISS implementation, solutions are implicitly obtained for the BTCS, Upwind and Crank–Nicolson schemes. The ADEISS uses iterative spreadsheet solution technique. Thus, it is not required a solution of simultaneous equations for each time step using matrix algebra. Two examples which, have the numerical and analytical solutions in literature, are solved in order to test the ADEISS performance. Both examples are solved for three schemes. It has been determined that the Crank–Nicolson scheme is in good agreement with the analytical solution; however the results of the BTCS and the Upwind schemes are lower than the analytical solution. The Upwind scheme suffers from considerably numerical diffusion whereas the BTCS scheme does not produce numerical diffusion. Thus, it provided better results than Upwind scheme which are closer to analytical results depending on the selected parameters. The ADEISS implementation is a computationally convenient procedure for the three well-known methods in the literature: The BTCS, Upwind and Crank–Nicolson.     

A mental model perspective for tool development and paradigm shift in spreadsheets

To address the problem of errors in spreadsheets, we have investigated spreadsheet authors׳ mental models in a hope of finding cognition-based principles for spreadsheet visualization and debugging tools. To this end, we have conducted three empirical studies. The first study explored the nature of mental models of spreadsheet authors during explaining and debugging tasks. It was found that several mental models about spreadsheets are activated in spreadsheet authors׳ minds. Particularly, when explaining a spreadsheet, the real-world and domain mental models are prominent, and the spreadsheet model is suppressed; however, when locating and fixing an error, one must constantly switch back and forth between the domain model and the spreadsheet model, which requires frequent use of the mapping between problem domain concepts and their spreadsheet model counterparts. The second study examined the effects of replacing traditional spreadsheet formulas with problem domain narratives in the context of a debugging task. Domain narratives were found to be easy to learn and they helped participants to locate more errors in spreadsheets. Furthermore, domain narratives also increased the use of the domain mental model and appeared to improve the mapping between the domain and spreadsheet models. The third study investigated the effects of allowing spreadsheet authors to fix errors by editing domain narratives, thus relieving them from the use of traditional low-level cell references. This scenario was found to promote spreadsheet authors using even more of their domain mental model in a manner that completely overshadowed the use of their spreadsheet mental model. Thus, from a mental model perspective, it is possible to devise a new spreadsheet paradigm that uses domain narratives in place of traditional spreadsheet formulas, thus automatically presenting spreadsheet content so that it prompts spreadsheet authors to think in a manner that closely corresponds to their mental models of the application domain.     

The use of spreadsheets in teaching undergraduate mathematics

The use of spreadsheets in teaching undergraduate mathematics is discussed, with examples of specific topics. The range of application includes Calculus, Differential Equations, Probability and Numerical Analysis. The advantages and disadvantages of spreadsheets are discussed.     

Reasoning about spreadsheets with labels and dimensions

Labels in spreadsheets can be exploited for finding formula errors in two principally different ways. First, the spatial relationships between labels and other cells express simple constraints on the cells usage in formulas. Second, labels can be interpreted as units of measurements to provide semantic information about the data being combined in formulas, which results in different kinds of constraints.In this paper we demonstrate how both approaches can be combined into an integrated analysis, which is able to find significantly more errors in spreadsheets than each of the individual approaches. In particular, the integrated system is able to detect errors that cannot be found by either of the individual approaches alone, which shows that the integrated system provides an added value beyond the mere combination of its parts. We also compare the effectiveness of this combined approach with several other conceivable combinations of the involved components and identify a system that seems most effective to find spreadsheet formula errors based on label and unit-of-measurement information.     

Learning constraints in spreadsheets and tabular data

Spreadsheets, comma separated value files and other tabular data representations are in wide use today. However, writing, maintaining and identifying good formulas for tabular data and spreadsheets can be time-consuming and error-prone. We investigate the automatic learning of constraints (formulas and relations) in raw tabular data in an unsupervised way. We represent common spreadsheet formulas and relations through predicates and expressions whose arguments must satisfy the inherent properties of the constraint. The challenge is to automatically infer the set of constraints present in the data, without labeled examples or user feedback. We propose a two-stage generate and test method where the first stage uses constraint solving techniques to efficiently reduce the number of candidates, based on the predicate signatures. Our approach takes inspiration from inductive logic programming, constraint learning and constraint satisfaction. We show that we are able to accurately discover constraints in spreadsheets from various sources.     

Automatic detection of dimension errors in spreadsheets

We present a reasoning system for inferring dimension information in spreadsheets. This system can be used to check the consistency of spreadsheet formulas and thus is able to detect errors in spreadsheets.Our approach is based on three static analysis components. First, the spatial structure of the spreadsheet is analyzed to infer a labeling relationship among cells. Second, cells that are used as labels are lexically analyzed and mapped to potential dimensions. Finally, dimension information is propagated through spreadsheet formulas. An important aspect of the rule system defining dimension inference is that it works bi-directionally, that is, not only “downstream” from referenced arguments to the current cell, but also “upstream” in the reverse direction. This flexibility makes the system robust and turns out to be particularly useful in cases when the initial dimension information that can be inferred from headers is incomplete or ambiguous.We have implemented a prototype system as an add-in to Excel. In an evaluation of this implementation we were able to detect dimension errors in almost 50% of the investigated spreadsheets, which shows (i) that the system works reliably in practice and (ii) that dimension information can be well exploited to uncover errors in spreadsheets.     

Learning spreadsheets: human instruction vs. computer-based instruction

Abstract

The increasing need to instruct students in the use of personal computer software, especially electronic spreadsheets, is placing greater demands on the already full university curriculum. A potential help in meeting these demands is the readily available computer-based software tutorials. In order to explore the feasibility of computer-based instruction as an alternative to human instruction, this research compares two modes of instruction, computer-based and human. An experiment was conducted with groups of business student subjects. The research results indicate no difference in students‘ attitude towards computer-based instruction and human instruction of spreadsheets. Students’ short-term recall of the software syntax being taught is as strong with computer-based instruction as with human instruction. These results were not affected by the level of personal computer experience that students had prior to the experiment. However, the ability to comprehend and immediately apply the software to a task is greater with human instruction than with computer-aided instruction. This advantage holds true for students instructed by experienced and inexperienced instructors.     

Self-instructive spreadsheets: an environment for automatic knowledge acquisition and tutor generation

Typically, spreadsheet applications are difficult to use for casual users (different from developers), mainly because of lack of support. In fact, building a tutoring facility for such applications is a time-consuming task. Our aim is the realization of a tool for the automatic generation of Intelligent Tutors for conventional spreadsheet applications. We have developed a system that works in two steps. In the first one, it extracts an explicit representation of the problem-solving pattern coded in a programmed spreadsheet. In the second step, it generates a hypertextual guide and an interactive tutor that can effectively support, in the native environment, the casual user of the spreadsheet with the specific application it is designed for. We have successfully tested our system on a class of students using an application for budget analysis.     

Teaching statistics with Excel 2007 and other spreadsheets

This article considers which activities in teaching statistics may be suitable candidates for the application of spreadsheets, and whether spreadsheets in general and Excel 2007 in particular are suitable for these tasks.     

Graphical inference for Infovis

How do we know if what we see is really there? When visualizing data, how do we avoid falling into the trap of apophenia where we see patterns in random noise? Traditionally, infovis has been concerned with discovering new relationships, and statistics with preventing spurious relationships from being reported. We pull these opposing poles closer with two new techniques for rigorous statistical inference of visual discoveries. The "Rorschach" helps the analyst calibrate their understanding of uncertainty and "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure.