Identity, addressing, authenticity and audit requirements for trust in ERP, analytics and big/open data in a “Cloud” computing environment : a review and proposal

Enterprises, both public and private, have rapidly commenced using the benefits of enterprise resource planning (ERP) combined with business analytics and “open data sets” which are often outside the control of the enterprise to gain further efficiencies, build new service operations and increase business activity. In many cases, these business activities are based around relevant software systems hosted in a “cloud computing” environment. “Garbage in, garbage out”, or “GIGO”, is a term long used to describe problems in unqualified dependency on information systems, dating from the 1960s. However, a more pertinent variation arose sometime later, namely “garbage in, gospel out” signifying that with large scale information systems, such as ERP and usage of open datasets in a cloud environment, the ability to verify the authenticity of those data sets used may be almost impossible, resulting in dependence upon questionable results. Illicit data set “impersonation” becomes a reality. At the same time the ability to audit such results may be an important requirement, particularly in the public sector. This paper discusses the need for enhancement of identity, reliability, authenticity and audit services, including naming and addressing services, in this emerging environment and analyses some current technologies that are offered and which may be appropriate. However, severe limitations to addressing these requirements have been identified and the paper proposes further research work in the area.

Trust in merged ERP and open data schemes in the “Cloud”

Enterprise resource planning (ERP) systems are rapidly being combined with “big data” analytics processes and publicly available “open data sets”, which are usually outside the arena of the enterprise, to expand activity through better service to current clients as well as identifying new opportunities. Moreover, these activities are now largely based around relevant software systems hosted in a “cloud computing” environment. However, the over 50- year old phrase related to mistrust in computer systems, namely “garbage in, garbage out” or “GIGO”, is used to describe problems of unqualified and unquestioning dependency on information systems. However, a more relevant GIGO interpretation arose sometime later, namely “garbage in, gospel out” signifying that with large scale information systems based around ERP and open datasets as well as “big data” analytics, particularly in a cloud environment, the ability to verify the authenticity and integrity of the data sets used may be almost impossible. In turn, this may easily result in decision making based upon questionable results which are unverifiable. Illicit “impersonation” of and modifications to legitimate data sets may become a reality while at the same time the ability to audit any derived results of analysis may be an important requirement, particularly in the public sector. The pressing need for enhancement of identity, reliability, authenticity and audit services, including naming and addressing services, in this emerging environment is discussed in this paper. Some current and appropriate technologies currently being offered are also examined. However, severe limitations in addressing the problems identified are found and the paper proposes further necessary research work for the area. (Note: This paper is based on an earlier unpublished paper/presentation “Identity, Addressing, Authenticity and Audit Requirements for Trust in ERP, Analytics and Big/Open Data in a ‘Cloud’ Computing Environment: A Review and Proposal” presented to the Department of Accounting and IT, College of Management, National Chung Chen University, 20 November 2013.)

The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection I. Two-way quantitative data

Data associated with germplasm collections are typically large and multivariate with a considerable number of descriptors measured on each of many accessions. Pattern analysis methods of clustering and ordination have been identified as techniques for statistically evaluating the available diversity in germplasm data. While used in many studies, the approaches have not dealt explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions). To consider the application of these techniques to germplasm evaluation data, 11328 accessions of groundnut (Arachis hypogaea L) from the International Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the rainy and post-rainy growing seasons were used. The ordination technique of principal component analysis was used to reduce the dimensionality of the germplasm data. The identification of phenotypically similar groups of accessions within large scale data via the computationally intensive hierarchical clustering techniques was not feasible and non-hierarchical techniques had to be used. Finite mixture models that maximise the likelihood of an accession belonging to a cluster were used to cluster the accessions in this collection. The patterns of response for the different growing seasons were found to be highly correlated. However, in relating the results to passport and other characterisation and evaluation descriptors, the observed patterns did not appear to be related to taxonomy or any other well known characteristics of groundnut.

The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection. II. Two-way data with mixed data types

As a sequel to a paper that dealt with the analysis of two-way quantitative data in large germplasm collections, this paper presents analytical methods appropriate for two-way data matrices consisting of mixed data types, namely, ordered multicategory and quantitative data types. While various pattern analysis techniques have been identified as suitable for analysis of the mixed data types which occur in germplasm collections, the clustering and ordination methods used often can not deal explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions) with incomplete information. However, it is shown that the ordination technique of principal component analysis and the mixture maximum likelihood method of clustering can be employed to achieve such analyses. Germplasm evaluation data for 11436 accessions of groundnut (Arachis hypogaea L.) from the International Research Institute of the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the post-rainy season and five ordered multicategory descriptors were used. Pattern analysis results generally indicated that the accessions could be distinguished into four regions along the continuum of growth habit (or plant erectness). Interpretation of accession membership in these regions was found to be consistent with taxonomic information, such as subspecies. Each growth habit region contained accessions from three of the most common groundnut botanical varieties. This implies that within each of the habit types there is the full range of expression for the other descriptors used in the analysis. Using these types of insights, the patterns of variability in germplasm collections can provide scientists with valuable information for their plant improvement programs.

Participatory Data Analytics: Collaborative interfaces for data composition and visualisation

This research proposes the development of interfaces to support collaborative, community-driven inquiry into data, which we refer to as Participatory Data Analytics. Since the investigation is led by local communities, it is not possible to anticipate which data will be relevant and what questions are going to be asked. Therefore, users have to be able to construct and tailor visualisations to their own needs. The poster presents early work towards defining a suitable compositional model, which will allow users to mix, match, and manipulate data sets to obtain visual representations with little-to-no programming knowledge. Following a user-centred design process, we are subsequently planning to identify appropriate interaction techniques and metaphors for generating such visual specifications on wall-sized, multi-touch displays.

A distributed computing framework for all-to-all comparison problems

Distributed computation and storage have been widely used for processing of big data sets. For many big data problems, with the size of data growing rapidly, the distribution of computing tasks and related data can affect the performance of the computing system greatly. In this paper, a distributed computing framework is presented for high performance computing of All-to-All Comparison Problems. A data distribution strategy is embedded in the framework for reduced storage space and balanced computing load. Experiments are conducted to demonstrate the effectiveness of the developed approach. They have shown that about 88% of the ideal performance capacity have be achieved in multiple machines through using the approach presented in this paper.

Data as an asset : what the upstream oil & gas industry can learn about ‘big data’ from companies like Facebook

The upstream oil & gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data”—that is, the ability to apply more sophisticated types of analytical tools to information in a way that extracts new insights or creates new forms of value—is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil & gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This paper examines existing data management practices in the upstream oil & gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the Big Data revolution. The comparison shows that, in companies that are leading the Big Data revolution, data is regarded as a valuable asset. The presented evidence also shows, however, that this is usually not true within the oil & gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how upstream oil & gas companies could potentially extract more value from data, and concludes with a series of specific technical and management-related recommendations to this end.

On the need for an international effort to capture, share and use crystallization screening data

When crystallization screening is conducted many outcomes are observed but typically the only trial recorded in the literature is the condition that yielded the crystal(s) used for subsequent diffraction studies. The initial hit that was optimized and the results of all the other trials are lost. These missing results contain information that would be useful for an improved general understanding of crystallization. This paper provides a report of a crystallization data exchange (XDX) workshop organized by several international large-scale crystallization screening laboratories to discuss how this information may be captured and utilized. A group that administers a significant fraction of the worlds crystallization screening results was convened, together with chemical and structural data informaticians and computational scientists who specialize in creating and analysing large disparate data sets. The development of a crystallization ontology for the crystallization community was proposed. This paper (by the attendees of the workshop) provides the thoughts and rationale leading to this conclusion. This is brought to the attention of the wider audience of crystallographers so that they are aware of these early efforts and can contribute to the process going forward. © 2012 International Union of Crystallography All rights reserved.

Integrating field survey data with satellite image data to improve shallow water seagrass maps : the role of AUV and snorkeller surveys

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (< 5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass is often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous Underwater Vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (< 5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photos of the seagrass were collected along transects via snorkelling or an AUV. Photos from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller collected field data sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison to snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5 % of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programs.

Data as an asset: what the oil and gas sector can learn from other industries about “Big Data”

The upstream oil and gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data” is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil and gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This viewpoint examines existing data management practices in the upstream oil and gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the way in Big Data. The comparison shows that, in companies that are widely considered to be leaders in Big Data analytics, data is regarded as a valuable asset—but this is usually not true within the oil and gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how the industry could potentially extract more value from data, and concludes with a series of policy-related questions to this end.

Regional avian species declines estimated from volunteer-collected long-term data using List Length Analysis

Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the "list length," can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes. List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization. © 2010 by the Ecological Society of America.

The validity of personal disturbance scale (DSSI/sAD) in people with diabetes mellitus, using longitudinal data

Despite being used since 1976, Delusions-Symptoms-States-Inventory/states of Anxiety and Depression (DSSI/sAD) has not yet been validated for use among people with diabetes. The aim of this study was to examine the validity of the personal disturbance scale (DSSI/sAD) among women with diabetes using Mater-University of Queensland Study of Pregnancy (MUSP) cohort data. The DSSI subscales were compared against DSM-IV disorders, the Mental Component Score of the Short Form 36 (SF-36 MCS), and Center for Epidemiologic Studies Depression Scale (CES-D). Factor analyses, odds ratios, receiver operating characteristic (ROC) analyses and diagnostic efficiency tests were used to report findings. Exploratory factor analysis and fit indices confirmed the hypothesized two-factor model of DSSI/sAD. We found significant variations in the DSSI/sAD domain scores that could be explained by CES-D (DSSI-Anxiety: 55%, DSSI-Depression: 46%) and SF-36 MCS (DSSI-Anxiety: 66%, DSSI-Depression: 56%). The DSSI subscales predicted DSM-IV diagnosed depression and anxiety disorders. The ROC analyses show that although the DSSI symptoms and DSM-IV disorders were measured concurrently the estimates of concordance remained only moderate. The findings demonstrate that the DSSI/sAD items have similar relationships to one another in both the diabetes and non-diabetes data sets which therefore suggest that they have similar interpretations.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

An extravariation model for improving confidence intervals of population size estimates from removal data

We propose a new model for estimating the size of a population from successive catches taken during a removal experiment. The data from these experiments often have excessive variation, known as overdispersion, as compared with that predicted by the multinomial model. The new model allows catchability to vary randomly among samplings, which accounts for overdispersion. When the catchability is assumed to have a beta distribution, the likelihood function, which is refered to as beta-multinomial, is derived, and hence the maximum likelihood estimates can be evaluated. Simulations show that in the presence of extravariation in the data, the confidence intervals have been substantially underestimated in previous models (Leslie-DeLury, Moran) and that the new model provides more reliable confidence intervals. The performance of these methods was also demonstrated using two real data sets: one with overdispersion, from smallmouth bass (Micropterus dolomieu), and the other without overdispersion, from rat (Rattus rattus).