The GIS and data solution for advanced business analysis

The GIS Business Analyst is a suite of Geographic Information System (GIS)-enabled tools, wizards, and data that provides business professionals with a complete solution for site evaluation, selective customer profiling, and trade area market analysis. Running simple reports, mapping the results, and performing complex probability models are among the capabilities The GIS Business Analyst offers in one affordable desktop analysis solution. Data and analyses produced by The GIS Business Analyst can be shared across departments, reducing redundant research and marketing efforts, speeding analysis of results, and increasing employee efficiency. The GIS Business Analyst is the first suite of tools for unlocking the intelligence of geography, demographic, consumer lifestyle, and business data. It is a valuable asset for business decision making such as analyzing market share and competition, determining new site expansions or reductions, and targeting new customers. The ability to analyze and visualize the geographic component of business data reveals trends, patterns, and opportunities hidden in tabular data. By combining information, such as sales data of the organization, customer information, and competitor locations, with geographic data, such as demographics, territories, or store locations, the GIS Business Analyst helps the user better understand organization market, organization customers, and organization competition. The business intelligence systems bring geographic information systems, marketing analysis tools, and demographic data products together to offer the user powerful ways to compete in today's business strategies.Geographical Informatic Systems, business analysis

Privacy Auctions for Recommender Systems

We study a market for private data in which a data analyst publicly releases a statistic over a database of private information. Individuals that own the data incur a cost for their loss of privacy proportional to the differential privacy guarantee given by the analyst at the time of the release. The analyst incentivizes individuals by compensating them, giving rise to a \emph{privacy auction}. Motivated by recommender systems, the statistic we consider is a linear predictor function with publicly known weights. The statistic can be viewed as a prediction of the unknown data of a new individual, based on the data of individuals in the database. We formalize the trade-off between privacy and accuracy in this setting, and show that a simple class of estimates achieves an order-optimal trade-off. It thus suffices to focus on auction mechanisms that output such estimates. We use this observation to design a truthful, individually rational, proportional-purchase mechanism under a fixed budget constraint. We show that our mechanism is 5-approximate in terms of accuracy compared to the optimal mechanism, and that no truthful mechanism can achieve a $2-\varepsilon$ approximation, for any $\varepsilon > 0$

ICAP: An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. The algorithm interfaces the rapid numerical processing capacity of a computer with the human ability to integrate qualitative information. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters and the analyst, who evaluate and elect to modify the cluster structure. Clusters can be deleted or lumped pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The ICAP was implemented in APL (A Programming Language), an interactive computer language. The flexibility of the algorithm was evaluated using data from different LANDSAT scenes to simulate two situations: one in which the analyst is assumed to have no prior knowledge about the data and wishes to have the clusters formed more or less automatically; and the other in which the analyst is assumed to have some knowledge about the data structure and wishes to use that information to closely supervise the clustering process. For comparison, an existing clustering method was also applied to the two data sets

How to Obtain University Data

Obtaining data from university sources can be a necessary step for accreditation or measurement purposes. Often, a request for data leaves questions for the database analyst. This can result in data assumptions that may skew results, and create the need for multiple iterations of data collection. This session will relay best practices and tips on how to retrieve correct university data as quickly as possible

Selling Privacy at Auction

We initiate the study of markets for private data, though the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing. In this paper, we propose to build such a theory. Specifically, we consider a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic. The analyst wishes to obtain an accurate estimate cheaply. On the other hand, the owners of the private data experience some cost for their loss of privacy, and must be compensated for this loss. Agents are selfish, and wish to maximize their profit, so our goal is to design truthful mechanisms. Our main result is that such auctions can naturally be viewed and optimally solved as variants of multi-unit procurement auctions. Based on this result, we derive auctions for two natural settings which are optimal up to small constant factors: 1. In the setting in which the data analyst has a fixed accuracy goal, we show that an application of the classic Vickrey auction achieves the analyst's accuracy goal while minimizing his total payment. 2. In the setting in which the data analyst has a fixed budget, we give a mechanism which maximizes the accuracy of the resulting estimate while guaranteeing that the resulting sum payments do not exceed the analysts budget. In both cases, our comparison class is the set of envy-free mechanisms, which correspond to the natural class of fixed-price mechanisms in our setting. In both of these results, we ignore the privacy cost due to possible correlations between an individuals private data and his valuation for privacy itself. We then show that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy.Comment: Extended Abstract appeared in the proceedings of EC 201

Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

The economic return on investment of a commercial photovoltaic system depends greatly on its performance over the long term and, hence, its degradation rate. Many methods have been proposed for assessing system degradation rates from outdoor performance data. However, comparing reported values from one analyst and research group to another requires a common baseline of performance; consistency between methods and analysts can be a challenge. An interlaboratory study was conducted involving different volunteer analysts reporting on the same photovoltaic performance data using different methodologies. Initial variability of the reported degradation rates was so high that analysts could not come to a consensus whether a system degraded or not. More consistent values are received when written guidance is provided to each analyst. Further improvements in analyst variance was accomplished by using the free open-source software RdTools, allowing a reduction in variance between analysts by more than two orders of magnitude over the first round, where multiple analysis methods are allowed. This article highlights many pitfalls in conducting 'routine' degradation analysis, and it addresses some of the factors that must be considered when comparing degradation results reported by different analysts or methods

DBPQL: A view-oriented query language for the Intel Data Base Processor

An interactive query language (BDPQL) for the Intel Data Base Processor (DBP) is defined. DBPQL includes a parser generator package which permits the analyst to easily create and manipulate the query statement syntax and semantics. The prototype language, DBPQL, includes trace and performance commands to aid the analyst when implementing new commands and analyzing the execution characteristics of the DBP. The DBPQL grammar file and associated key procedures are included as an appendix to this report

Micro-based fact collection tool user's manual

A procedure designed for use by an analyst to assist in the collection and organization of data gathered during the interview processes associated with system analysis and modeling task is described. The basic concept behind the development of this tool is that during the interview process an analyst is presented with assertions of facts by the domain expert. The analyst also makes observations of the domain. These facts need to be collected and preserved in such a way as to allow them to serve as the basis for a number of decision making processes throughout the system development process. This tool can be thought of as a computerization of the analysts's notebook