GEORDi: Supporting lightweight end-user authoring and exploration of Linked Data

The US and UK governments have recently made much of the data created by their various departments available as data sets (often as csv files) available on the web. Known as ”open data” while these are valuable assets, much of this data remains useless because it is effectively inaccessible for citizens to access for the following reasons: (1) it is often a tedious, many step process for citizens simply to find data relevant to a query. Once the data candidate is located, it often must be downloaded and opened in a separate application simply to see if the data that may satisfy the query is contained in it. (2) It is difficult to join related data sets to create richer integrated information (3) it is particularly difficult to query either a single data set, and even harder to query across related data sets. (4) To date, one has had to be well versed in semantic web protocols like SPARQL, RDF and URI formation to integrate and query such sources as reusable linked data. Our goal has been to develop tools that will let regular, non-programmer web citizens make use of this Web of Data. To this end, we present GEORDi, a set of integrated tools and services that lets citizen users identify, explore, query and represent these open data sources over the web via Linked Data mechanisms. In this paper we describe the GEORDi process of authoring new and translating existing open data in a linkable format, GEORDi’s lens mechanism for rendering rich, plain language descriptions and views of resources, and the GEORDI link-sliding paradigm for data exploration. With these tools we demonstrate that it is possible to make the Web of open (and linked) data accessible for ordinary web citizen users

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin

ERRORS IN SPREADSHEET USE

Fourteen experienced users of two commercial spreadsheet packages, Lotus 123 and Multiplan, performed four tasks - two of entering spreadsheets and two of modifying those same spreadsheets. Their actions were videotaped and analyzed for incidents of errors. Over 450 errors were made, the majority of them centered around the visual properties of the spreadsheet packages. A classification of the errors is presented with an analysis of the causes governing the production of the errors. A discussion of the choices in the design of the interface which facilitated the production of these errors is also presented.Information Systems Working Papers Serie

An integrated decision support environment for organisational decision making

Traditional decision support systems are based on the paradigm of a single decision maker working at a stand-alone computer or terminal who has a specific decision to make with a specific goal in mind. Organisational decision support systems aim to support decision makers at all levels of an organisation (from executive, middle management managers to operators), who have a variety of decisions to make, with different priorities, often in a distributed environment. Such systems are designed and developed with extra functionality to meet the challenge. This paper proposes an Integrated Decision Support Environment (IDSE) for organisational decision making. The IDSE is designed and developed based on distributed client/server networking, with a combination of tight and loose integration approaches for information exchange and communication. The prototype of the IDSE demonstrates a good balance between flexibility and reliability

Dimension checking tools for spreadsheets

We present the evolution of a reasoning system for inferring dimension information in spreadsheets. The three papers included in this thesis show how the initial system can be used to check the consistency of spreadsheet formulas and thus is able to detect errors in spreadsheets, and the evolution to a system that can check both label and dimension errors. The approach for these systems is based on three static analysis components. First, the spatial structure of the spreadsheet is analyzed to infer the labels for specific cells. Second, those cells that are identified as labels are analyzed to determine dimension information. Once this is completed the system, will look at formulas and, using specific rules, will determine if the dimensions and labels are correct. 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. These systems have been implemented as a add-in for Excel, and this prototype has allowed us to perform several evaluations on the systems. These evaluations show that the systems can be effective in detecting dimension errors, with the initial system detecting errors in 50% of the investigated spreadsheets, and the subsequent systems having similar success. In addition these evaluations show that by adding label checking, the effectiveness and efficiency of the system is improved with many previously undetected errors being found