Enterprise Datasets Research Articles

Explainable enterprise credit rating using deep feature crossing

Deep Neural Networks (DNNs) have powerful learning abilities on high-rank and non-linear features, and thus have been applied to various fields, exhibiting higher discrimination performance than conventional methods. However, their applications in enterprise credit rating tasks are rare, as most DNNs employ the “end-to-end” learning paradigm, producing high-rank representations of objects or predictive results without any explanations. This “black box” approach makes it difficult for users in the financial industry to understand how these predictive results are generated, or what correlations exist with the raw inputs, leading to a lack of trust to the predictions. To address this issue, this paper proposes a novel network to explicitly model the enterprise credit rating problem using DNNs and attention mechanisms, allowing for explainable enterprise credit ratings. Experiments conducted on real-world enterprise datasets show that the proposed approach achieves higher performance than conventional methods, while also providing insights into individual rating results and the reliability of model training. The code is provided on .

The ‘play’ of institutions and firm investment: Evidence from a transition economy

AbstractAccording to recent literature in new institutional economics and entrepreneurship, institutions, including formal and informal ones (i.e., the rules) are important. However, in a homogeneous national institutional environment, it is unclear whether and how the execution of institutions (how the rules are implemented) matters. The purpose of this research is to assess the mechanisms that local governments can use to encourage entrepreneurial investment, which determines the future growth potential of small businesses. We find that (i) improving local institutional quality in general boosts entrepreneurial investment; and (ii) more financially constrained firms are more sensitive to informal local governance, while less financially constrained firms are more sensitive to local formal institutions, using a large‐scale small and medium‐sized enterprise (SME) dataset from Vietnam from 2006 to 2019.

A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems

Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they can cause queue congestion. This paper models data interchange (EDI) messages. We sought to understand how best DevOps should model these messages for performance testing and how best to apply smart EDI content awareness that enhance the realms of Ambient Intelligence (Aml) with a Business-to business (B2B) supply chain organisation. We considered key performance indicators (KPI) for over- or under-utilisation of these queueing systems. We modelled message service and inter-arrival times, partitioned data along various axes to facilitate statistical modelling and used continuous parametric and non-parametric techniques. Our results include the best fit for parametric and non-parametric techniques. We noted that a one-size-fits-all model is inappropriate for this heavy-tailed enterprise dataset. Our results showed that parametric distribution models were suitable for modelling the distribution’s tail, whilst non-parametric kernel density estimation models were better suited for modelling the head of a distribution. Depending on how we partitioned our data along the axes, our data suffer from quantisation noise.

Female Corporate Leadership and Firm Growth Strategy: A Global Perspective

This study aims to understand the relationship between female corporate leadership and firm performance based on exploration and exploitation strategies using a global enterprise data set. Previous studies report conflicting evidence of female corporate leadership on firm performance. This study applies an exploration–exploitation framework and suggests that the relative advantage of female corporate leadership relies upon certain aspects of the firm’s growth strategy. The empirical evidence confirms that the relative advantage of female corporate leadership resides more in the exploitation than the exploration aspect of a firm’s growth strategy compared to male corporate leadership. The study thus offers important implications for broader business practices when considering the alignment between the choice of corporate leadership and firm growth strategy.

Distance traveled to undergo revision shoulder arthroplasty is impacted by patient demographics and exceeds that of primary shoulder arthroplasty

BackgroundPrevious research has highlighted disparities in access to and outcomes following shoulder arthroplasty. The purpose of this study is to compare travel distance for primary vs. revision surgery and to determine the relationships between travel distance to undergo revision shoulder arthroplasty and patient demographics and postoperative adverse outcomes. We hypothesized that older patients, those with increased medical comorbidities, and those with greater financial resources would travel farther for surgery. MethodsThe Healthcare Cost and Utilization Project, American Hospital Association, and UnitedStatesZipCodes.org Enterprise data sets were compiled to collect data on patient demographics, operation performed, and postoperative adverse outcomes. Population-weighted zip code centroid points of patients’ residences as well as hospital coordinates were used to approximate the distance traveled to undergo shoulder arthroplasty. Kruskal-Wallis tests and logistic regression analyses were used to analyze the relationship between travel distance and patients’ demographics and postoperative outcomes. ResultsPatients traveled farther to undergo revision shoulder arthroplasty than to undergo primary shoulder arthroplasty (P = .0001). Among patients who underwent revision operations, older age (P = .0001) and increased medical comorbidities (P = .003) were associated with decreased travel distance. White patients and those with commercial insurance traveled farther to receive care (P = .0001). There were no clinically significant associations between postoperative outcomes following revision surgery and travel distance. ConclusionPatients may travel farther to undergo revision surgery owing to patient preferences or because these complex operations are performed at fewer centers. Younger and healthier patients, white patients, and those with commercial insurance plans traveled farther to receive care. Future work is needed to determine the underlying causes of these disparities and whether it is patient preferences or systemic factors such as hospital density or access to high-quality health insurance that are influencing access to this specialized surgical care.

Discrete-Events Simulation for Teaching Statistics in Industrial Engineering

This paper presents a discrete events simulation tool developed to support undergraduate students in their Statistics and Data Analysis course. Although the use of modern smart technologies in the industry contributes to a profusion of data, very few enterprise datasets are freely available, resulting in a serious lack of open real-world data for research and education. To overcome this difficulty, we designed a tool that simulates scheduling scenarios in a manufacturing environment. The generated data may be used to put statistical concepts and methods into practice to design cost-effective strategies for optimizing key performance indicators, such as reducing production time, improving quality, eliminating wastes, and maximizing profits. Keywords: industrial datasets, teaching statistics, discrete events simulation

A demonstration of KGLac

Data science growing success relies on knowing where a relevant dataset exists, understanding its impact on a specific task, finding ways to enrich a dataset, and leveraging insights derived from it. With the growth of open data initiatives, data scientists need an extensible set of effective discovery operations to find relevant data from their enterprise datasets accessible via data discovery systems or open datasets accessible via data portals. Existing portals and systems suffer from limited discovery support and do not track the use of a dataset and insights derived from it. We will demonstrate KGLac, a system that captures metadata and semantics of datasets to construct a knowledge graph (GLac) interconnecting data items, e.g., tables and columns. KGLac supports various data discovery operations via SPARQL queries for table discovery, unionable and joinable tables, plus annotation with related derived insights. We harness a broad range of Machine Learning (ML) approaches with GLac to enable automatic graph learning for advanced and semantic data discovery. The demo will showcase how KGLac facilitates data discovery and enrichment while developing an ML pipeline to evaluate potential gender salary bias in IT jobs.

Basics of SQL for Audit Data Retrieval and Analysis

ABSTRACTThe purpose of this instructional resource is to explain how Structured Query Language (SQL) queries can help auditors to independently retrieve properly formatted data as audit evidence or for further analyses. It demonstrates data extraction using Microsoft (MS) Access, one of the simplest SQL-compliant database software applications. We use a dataset fragment that is extracted from the publicly available enterprise datasets provided by Walton College (University of Arkansas) to run SQL queries as a part of audit investigations. Data extraction is the first step of Extract, Transform, and Load (ETL) and may be time-consuming. We demonstrate how SQL queries can assist with this task, thereby allowing the auditor to begin analysis sooner. This instructional resource can be used to prepare future auditors for the emerging data-rich and technology-driven business environment.

The spatial integration and coordinated industrial development of urban agglomerations in the Yangtze River Economic Belt, China

Urban agglomeration is the engine of national development and regional prosperity. Although extensive work has investigated issues related to this new form of spatial governance, few studies have directly illustrated the spatial integration of urban agglomeration and its relationship with industrial development. This paper employs nighttime light data and industrial enterprise datasets to investigate the spatial integration and industrial development in the Yangtze River Economic Belt (YREB) of China for 1995–2015. We here illustrate the significant relationship between the spatial integration of urban agglomerations and the characteristics of industrial development. In the process of spatial integration, urban form, intercity relation and their evolution show clear regional differences. Because of the differences in socio-economic and geographical characteristics, urban systems are more advanced and closely related in developed areas. A significant negative (positive) spatial correlation between industrial specialization (diversification) and urban form is supported by using bivariate Moran's I, and spatial clustering patterns are clearly different across the three urban agglomerations. A panel regression reveals that intercity relations are significantly associated with the characteristics of industrial development. Higher levels of industrial diversification and competition are associated with weaker intercity relations, while industrial structures similarities are reversed. These findings could be used to formulate reasonable policies and plans and to support future regional spatial integration and coordinated development.

The Application of the Positive Semi-Definite Kernel Space for SVM in Quality Prediction

Background:A transformation toward 4th Generation Industrial Revolution (Industry 4.0) is being led by Germany based on Cyber-Physical System-enabled manufacturing and service innovation. Smart manufacturing is an important feature of Industry 4.0 which uses the networked manufacturing systems for smart production. Current manufacturing systems (5M1E systems) require deeper mining of the data which is generated from manufacturing process.Objective:To map low-dimensional embedding into the input space would meet the requirement of “kernel trick” to solve a problem in feature space. On the other hand, the distance can be calculated more precisely.Methods:In this research, we proposed a positive semi-definite kernel space by using a constant additive method based on a kernel view of ISOMAP. There were 6 steps in the algorithm.Results:The classification precision of KMLSVM was better than SVM in the enterprise data set, in which SVM selected the RBF kernel and optimized its parameters.Conclusion:We adopted the additive constant method in kernel space construction and the positive semi-definite kernel was built. The typical mixed data set of an enterprise was used in simulation. We compared the SVM and KMLSVM in this data set and optimized the SVM kernel function parameters. The simulation results demonstrated the KMLSVM was a better algorithm in mix type data set than SVM.

A Symmetric Searchable Encryption Identification of Data on Probabilistic Trapdoors

Accessible Encryption (SE) permits a client in accordance with transfer records in accordance with the astronaut and according to seem thru it of a faraway manner while defending the security concerning each the statistics yet the inquiries. Right now, entrust a generative then the simple in imitation of actualize Symmetric Searchable Encryption Scheme because instance (SSE) [4]. This tale takes the some round concerning correspondence namely O(n) instances on calculations upstairs n range regarding records . We likewise presented some other variety of Search Pattern Privacy, as gives a percentage about safety on the spillage structure trapdoor [4]. We also advocate the modifications over our graph because of batch inquire who can not reap the versatile vagary recipe. The current method offers the appropriateness whilst maintaining stuff about the data classified, that is viable so much such has advise bother among the full altar about the demand bill and wants in accordance with remove watchwords now the archives are eke out away. We likewise recommend adjustments in our format so the graph execute be utilized upon the potent enemies at the fee on various rounds of transmission yet the inclination space. [6]We may eke exhibit our layout on a range of commercial enterprise datasets. Likewise correct now, utilizes the Hash tying strategy instead than band over encryption action because file for consideration majority which makes such splendid for light poise applications. We are the first in imitation of suggest pardon trapdoors between Symmetric Searchable Encryption because the tussock search.

Clustering social audiences in business information networks

Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.

Forecasting leading industry stock prices based on a hybrid time-series forecast model.

Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices. In the proposed model, stepwise regression is first adopted, and multivariate adaptive regression splines and kernel ridge regression are then used to select the key features. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. The collected stock prices are employed to verify the proposed model under accuracy. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors.

Enterprise credit risk evaluation based on neural network algorithm

To explore the enterprise credit risk evaluation, the application effect of several common neural network models in Chinese small and medium-sized enterprise data sets was compared and the optimal parameters for each model were determined. In addition, the classification accuracy and the applicability of the model were compared, and finally the common problem of optimization neural network algorithm based on population was solved: need to determine the dimensions in advance. The experimental results showed that the probabilistic neural network (PNN) had the minimum error rate and second types of errors, while the PNN model had the highest AUC value and was robust. To sum up, the algorithm makes some contributions to solve the financing problem of small and medium-sized enterprises in China.

Obscured by the cloud: A resource allocation framework to model cloud outage events

Abstract As Small Medium Enterprises (SMEs) adopt Cloud technologies to provide high value customer offerings, uptime is considered important. Cloud outages represent a challenge to SMEs and micro teams to maintain a services platform. If a Cloud platform suffers from downtime this can have a negative effect on business revenue. Additionally, outages can divert resources from product development/delivery tasks to reactive remediation. These challenges are immediate for SMEs or micro teams with a small levels of resources. In this paper we present a framework that can model the arrival of Cloud outage events. This framework can be used by DevOps teams to manage their scarce pool of resources to resolve outages, thereby minimising impact to service delivery. We analysed over 300 Cloud outage events from an enterprise data set. We modelled the inter-arrival and service times of each outage event and found a Pareto and a lognormal distribution to be a suitable fit. We used this result to produce a special case of the G/G/1 queue system to predict busy times of DevOps personnel. We also investigated dependence between overlapping outage events. Our predictive queuing model compared favourably with observed data, 72% precision was achieved using one million simulations.

Toward Beneficial Transformation of Enterprise Workloads to Hybrid Clouds

With the promise of providing flexible and elastic computing resources on demand, the cloud computing has been attracting enterprises and individuals to migrate workloads in the legacy environment to the public/private/hybrid clouds. However, the workload migration is often interpreted as an image migration or re-installation/data copying as the exact snapshot of the source machine, and the various cloud platforms and service models are rarely taken into consideration during migration planning. Thus, the cloud migration techniques have not provided enough options that can satisfy the various migration requirements. In this paper, we propose a model to tackle the migration challenges that transforms one resource into the same or another resource in hybrid clouds. We formulate the problem as a constraint satisfaction problem, and iteratively decompose the server components and consolidate the servers. Furthermore, we propose a compute-network mapping algorithm to match computing resources with network resources to guarantee network affinity. The ultimate goal is to recommend the optimal target cloud platform with network affinity and the minimum cost. Through the evaluation of the proposed model using real enterprise datasets (up to 2012 machines), we prove that the proposed model satisfies the goal. We show that when migrating into virtualized cloud environments, thorough resource planning can reduce 16% of current resources, 5%–10% servers can be consolidated, and more than 60% servers are possible candidates for server decomposition.

Visual query interfaces for semantic datasets: An evaluation study

The rapid growth of the Linked Open Data cloud, as well as the increasing ability to lift relational enterprise datasets to a semantic, ontology-based level means that vast amounts of information are now available in a representation that closely matches the conceptualizations of the potential users of this information. This makes it interesting to create ontology based, user-oriented tools for searching and exploring this data. Although initial efforts were intended for tech users with knowledge of SPARQL/RDF, there are ongoing proposals designed for lay users. One of the most promising approaches is to use visual query interfaces, but more user studies are needed to assess their effectiveness. In this paper, we compare the effect on usability of two important paradigms for ontology-based query interfaces: form-based and graph-based interfaces. In order to reduce the number of variables affecting the comparison, we performed a user study with two state-of-the-art query tools developed by ourselves, sharing a large part of the code base: the graph-based tool OptiqueVQS*, and the form-based tool PepeSearch. We evaluated these tools in a formal comparison study with 15 participants searching a Linked Open Data version of the Norwegian Company Registry. Participants had to respond to 6 non-trivial search tasks using alternately OptiqueVQS* and PepeSearch. Even without previous training, retrieval performance and user confidence were very high, thus suggesting that both interface designs are effective for searching RDF datasets. Expert searchers had a clear preference for the graph-based interface, and mainstream searchers obtained better performance and confidence with the form-based interface. While a number of participants spontaneously praised the capability of the graph interface for composing complex queries, our results evidence that graph interfaces are difficult to grasp. In contrast, form interfaces are more learnable and relieve problems with disorientation for mainstream users. We have also observed positive results introducing faceted search and dynamic term suggestion in semantic search interfaces.

Visual Query Interfaces for Semantic Datasets: An Evaluation Study

The rapid growth of the Linked Open Data cloud, as well as the increasing ability to lift relational enterprise datasets to a semantic, ontology-based level means that vast amounts of information are now available in a representation that closely matches the conceptualizations of the potential users of this information. This makes it interesting to create ontology based, user-oriented tools for searching and exploring this data. Although initial efforts were intended for tech users with knowledge of SPARQL/RDF, there are ongoing proposals designed for lay users. One of the most promising approaches is to use visual query interfaces, but more user studies are needed to assess their effectiveness. In this paper, we compare the effect on usability of two important paradigms for ontology-based query interfaces: form-based and graph-based interfaces. In order to reduce the number of variables affecting the comparison, we performed a user study with two state-of-the-art query tools developed by ourselves, sharing a large part of the code base: the graph-based tool OptiqueVQS*, and the form-based tool PepeSearch. We evaluated these tools in a formal comparison study with 15 participants searching a Linked Open Data version of the Norwegian Company Registry. Participants had to respond to 6 non-trivial search tasks using alternately OptiqueVQS* and PepeSearch. Even without previous training, retrieval performance and user confidence were very high, thus suggesting that both interface designs are effective for searching RDF datasets. Expert searchers had a clear preference for the graph-based interface, and mainstream searchers obtained better performance and confidence with the form-based interface. While a number of participants spontaneously praised the capability of the graph interface for composing complex queries, our results evidence that graph interfaces are difficult to grasp. In contrast, form interfaces are more learnable and relieve problems with disorientation for mainstream users. We have also observed positive results introducing faceted search and dynamic term suggestion in semantic search interfaces.

Inferring social status and rich club effects in enterprise communication networks.

Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels — voice call, short message, and email — to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a “rich club” maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.

The Data Deciders [chief data officers

As data IS increasingly perceived as a dynamic asset, more thought will be given to how best to manage it - not only in terms of storage, availability, quality and analytics, but also the architecture that allows for data being accessible and `malleable'. Bigger data management covers IT, business strategy, statistical analysis and more. Accordingly, the job title of chief data officer (CDO) is becoming more prevalent in executive hierarchies, although some estimates suggest that there are now only 150 CDOs in organisations around the world. The chief data officer is an emerging position tasked with overseeing projects that aim to derive value from enterprise data sets: some observers think that CDOs could assume a defining role in shaping corporate success for the next 10 years.