This paper uses aggregate-level data, as well as case-studies, to trace the evolution of some key structural features of the Indian economy, relating both to the agricultural and the informal industrial sector. These aggregate trends are used to infer: (a) the dominant relations of production under which the vast majority of the Indian working people labour, and (b) the predominant ways in which the surplus labour of the direct producers is appropriated by the dominant classes. This summary account is meant to inform and link up with on-going attempts at radically restructuring Indian society. JEL Categories: B24, B51

relations of production, modes of surplus extraction, India

Relations of Production and Modes of Surplus Extraction in India

This paper uses aggregate-level data, as well as case-studies, to trace out the evolution of some key structural features of the Indian economy, relating both to the agricultural and the informal industrial sector. These aggregate trends are used to infer: (a) the dominant relations of production under which the vast majority of the Indian working people labour, and (b) the predominant ways in which the surplus labour of the direct producers is appropriated by the dominant classes. This summary account is meant to inform and link up with on-going attempts at radically restructuring Indian society. JEL Categories: B24, B51relations of production, forms of surplus extraction, mode of production, India

Understanding Ecosystem Data

There is a growing body of empirical studies on business ecosystems. Driven by different questions these studies typically employ a wide variety of data sources – ranging from open to proprietary, structured to unstructured – that contain a broad range of entities, relationships, activities, and issues of interest. Individually, these data sources offer the ability to investigate very targeted business ecosystem questions. However, when linked and combined these data sources can potentially open up many new lines of inquiry. The purpose of this study is to provide an overview of the scope and complexity of the business ecosystem data landscape, discuss what type(s) of information is captured in them, identify how data sources overlap and differ, discuss strengths and weaknesses, and suggest new types of analyses that could be generated when combined. In doing so this study aims to help researchers and practitioners with the data identification and selection process and stimulate novel data-driven ecosystem intelligence. The study concludes with theoretical and managerial implications

Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we developed Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2018

The Ecosystem of Machine Learning Methods

Machine learning (ML) is a rapidly evolving field and plays an important role in today’s data-driven business environment. Many digital innovations in domains as diverse as healthcare, banking, energy, and retail are powered and enabled by ML. Examples include search engines, recommendation systems, pattern recognition, computer vision, and natural language processing. A key element in ML innovation is the advancement of the underlying methods, which specify how machines should algorithmically process, derive patterns, and learn from data for a given decisioning task. The speed at which this is happening is exponential, with researchers leveraging and building upon existing building blocks as well as introducing entirely new methods. Given the speed, scale, and complexity, understanding this complex evolving ML method space can be challenging. What methods are core and peripheral to ML? Which methods span task areas? How are ML methods evolving? In this exploratory research paper, I address these questions by (1) framing the ML method space and (2) visualizing the evolving structure of the ML methods ecosystem. The results reveal several foundational ML building blocks, different coupling levels between ML areas, and variable speeds of evolution. The study also provides insights into how digital innovation evolves at an algorithmic level. I discuss the implications of the findings and describe opportunities for future ML ecosystem-focused research

Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

Visualizing the Alliance Network Structure of Service Industries

A growing body of research focuses on the structure of interfirm value co-creation. Despite this emphasis, little is known about the variation in interfirm collaboration across different service industries. Building on prior work in service value networks and business ecosystems, we analyze the structural characteristics of 11 service industries using a data-driven visualization approach. We first examine the alliance network structure of each service industry individually and differentiate the nature of collaboration using an exploration/coopetition lens. Second, we examine service industries integratively, thereby exploring the extent to which service industries are converging and traditional industry boundaries are blurred. Our results reveal significant structural differences in alliance network structures between service industries as well as diverse value co-creation orientations. Our macro analysis reveals an overall core-periphery structure and different service industry coupling levels, with actors in the ICT industry playing a particularly central role across subclusters. We frame our findings in terms of industry robustness, openness, and embeddedness. We conclude the paper with theoretical and practical implications for understanding and managing service ecosystems and suggest future research opportunities

Visualizing Interfirm Collaboration in the Microservices Ecosystem

The shift from monolithic software solutions to a microservices architecture is fundamentally changing the way software is developed, deployed, and managed. In this paper, we aim to uncover the collaborative fabric of the microservices ecosystem using a data-driven visualization approach of 2,608 software firms. Our visual analysis reveals a core-periphery structure with several subcommunities, suggesting both complementary and competing arrangements between software vendors. Theoretically, our paper contributes to our understanding of interfirm relationships in a software context. Managerially, our results show that there are wide range of partnership strategies that shape the microservices ecosystem. Methodologically, we demonstrate how a data-driven ecosystem visualization approach can help decision makers augment their sensemaking capability of emerging software ecosystems. The paper concludes with opportunities for future research

Multifamily Malware Models

When training a machine learning model, there is likely to be a tradeoff between the accuracy of the model and the generality of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we obtain stronger results as compared to a case where we train a single model on multiple diverse families. During the detection phase, it would be more efficient to have a single model that could detect multiple families, rather than having to score each sample against multiple models. In this research, we conduct experiments to quantify the relationship between the generality of the training dataset and the accuracy of the resulting model within the context of the malware detection problem