1,318 research outputs found
Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing
No full textFull text access from Treasures at UT Dallas is restricted to current UTD affiliates.Scalability and privacy form two critical dimensions that will eventually determine the extent of the success of big data analytics. We present scalable approaches to address privacy concerns when sharing transactional databases. Although the benefits of sharing are well documented and the number of firms sharing transactional data has increased over the years, the rate at which this number has grown is not quite what it could have been. Concerns about revealing proprietary information have prevented some retailers from sharing, despite the obvious advantages in an increasingly networked economy. In the context of sharing transactional data, sensitive information is typically based on relationships derived from frequently occurring itemsets, result of surprisingly successful promotions by the retailer, or unexpected relationships identified by the retailer while mining the data. Prior work in this area includes optimal approaches based on integer programming to maximize the accuracy of shared databases, while hiding all sensitive itemsets. While these approaches were shown to solve problems involving up to 10 million transactions, many transactional databases in the big data context are considerably larger and the existing integer programming-based procedures do not scale well enough to solve these larger problems. Consequently, there is no effective solution procedure for such databases in extant literature.
In this paper, we first present an optimal procedure leveraging intuition from linear programming based column generation. Next, we identify a common structure that exists in these problems, and show how it can be taken advantage of through an approach based on sorting and column generation to make the process more efficient. We then illustrate how this structure can be incorporated into the column generation based procedure to develop an effective, scalable heuristic. Computational experiments are conducted on databases with 50 million and 100 million transactions, involving problems that could not be solved using existing optimal procedures. These experiments show that the optimal column generation based procedure can solve problem instances significantly larger than those tackled previously, and that the scalable heuristic identifies nearoptimal solutions quickly in all instances where the optimal solution is known. We investigate the impact of hiding sensitive itemsets on the quality of a rule-based recommender system derived from the shared data. As expected, recommendation quality decreases as the number of sensitive itemsets increases; however, recommendation accuracy stays above 80% of the original rate when using the unmodified data even when there are 1,000 sensitive itemsets to hide. The effect on recommendation accuracy from using the heuristic relative to the optimal approach was very small: the accuracies with the heuristic were over 97% of the corresponding accuracies with the optimal approach in every experiment, and over 99% in the vast majority.Naveen Jindal School of Managemen
SG-Experiment_Other-pleasing_choice
No full textA 'serious game' experiment was conducted on a group of graduate students to study the effect of a choice architecture on other-pleasing choice. Data for the control group (neutral treatment) is given on the first sheet, followed by the data for 'delight-seeking' and 'disappointment-averse' treatments in the next two sheets. Variables description is given on the last sheet of the workbook
sj-docx-1-pij-10.1177_13506501211039736 - Supplemental material for Tribological Characteristics of Thermomechanically Processed 7075 Al Alloy Through Nano-scratch Characterization
No full textSupplemental material, sj-docx-1-pij-10.1177_13506501211039736 for Tribological Characteristics of Thermomechanically Processed 7075 Al Alloy Through Nano-scratch Characterization by Souriddha Sanyal, Ashoktaru Chakraborty, Angshuman Sarkar, Susanta K Pradhan, Utpal Madhu, Sumit Chabri, Apurba Das and Arijit Sinha in Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology</p
Competing with the Sharing Economy: Incumbents’ Reaction on Review Manipulation
No full textThe emergence of the sharing economy has provided the market with an untapped wealth of supplies, posing a threat to incumbents. In response to competition from the sharing economy, incumbents must adjust their competitive strategies. In this paper, we focus our investigation on a nascent competitive strategy—consumer opinion manipulation—in the lodging sector of the hospitality industry. We examine two types of opinion manipulations through online reviews: promoting oneself and demoting one’s competitors. Combining data from Airbnb, Expedia, TripAdvisor, AirDNA, the Texas Comptroller’s Office, and Smith Travel Research, we estimate the impact of a new sharing economy entrant, Airbnb, on conventional hotels’ manipulation strategies by exploring the supply variation of the competing Airbnb listings around each hotel. We find that, intriguingly, hotels tend to reduce mutual demotion when facing the common “enemy” of Airbnb competition. However, there is considerable heterogeneity among hotels in response to Airbnb competition. Low-end hotels tend to not increase their review manipulation activities for purposes of either self-promotion or demotion, while high-end hotels tend to demote competing hotels less and promote themselves more in the presence of higher levels of Airbnb competition.This article is published as Cheng Nie, Zhiqiang Zheng, Sumit Sarkar (2022) “Manipulating Consumer Opinion: Incumbents Reaction to Competition from the Sharing Economy,” MIS Quarterly, (46:3), 1573–1602. https://doi.org/10.25300/MISQ/2022/15666. Posted with permission
Firm Competitive Structure and Consumer Reaction in Search Advertisings
No full textSponsored search advertising has become an important venue for firms competing for consumers. As a result, many keywords attract a large number of bidders, and the competing advertisers may be quite heterogeneous. We examine whether this heterogeneity impacts how consumers perceive and react to such competitions. To this end, we draw on the theory of strategic groups to prescribe the structure of the competitive environment and investigate how strategic groups impact consumers’ clicking and website-visit behavior when viewing sponsored search results. Our unique datasets that combine search results from Google and consumers’ clickstream data enable us to disentangle such an impact. We find strong positive externality for within-group competitors relative to across-group competitors: (1) consumers are more likely to co-visit two firms that belong to the same strategic group, as opposed to two firms from different groups when both firms appear in the search results; (2) the presence of a firm in the search results primes consumers to visit other firms from the same strategic group even when the other firms do not appear in the search results. Our findings contribute to the sponsored search and strategic group literature by theorizing and empirically verifying consumers’ website-visit behaviors from the strategic group perspective.This article is published as Nie, Cheng; Zheng, Zhiqiang (Eric); and Sarkar, Sumit (2024) "Firm Competitive Structure and Consumer Reaction in Search Advertisings," Journal of the Association for Information Systems, 25(2), 442-462.
DOI: 10.17705/1jais.00835. Available at: https://aisel.aisnet.org/jais/vol25/iss2/2. Posted with permission
A Strategic Group Analysis of Competitor Behavior in Search Advertising
No full textFirms compete intensely in sponsored search. Their bidding strategies hinge on understanding who competes with whom, how they compete, and how consumers react to competing advertisements. In this context, we investigate how firm competition impacts consumers’ click-through behaviors in search advertising from a strategic group perspective. Using search results from Google and consumers’ clickstream data, we found strong negative externality for competitors within the same strategic group relative to competitors across strategic groups: firms reap fewer click-throughs when an advertisement of another firm from the same strategic group is also displayed in search results, relative to when other displayed advertisers are not from the same group. This indicates that when competitors from the same strategic group are likely to appear in the results of a sponsored search auction, the focal firm would be better off avoiding head-to-head competition in the auction. However, we did not find empirical evidence of such firm behaviors, suggesting myopia or the inability of firms to avoid such competition. We also show that when multiple firms from the same strategic group appear in search results, the closer the focal firm is located to such competing firms, the more click-throughs the firm accrues. This suggests that firms should stay close to their within-group competitors when they compete in the same search auction. Further, our empirical results indicate that firms are indeed doing so. Using another set of data from Google AdWords reports, we show that our findings are also robust to multi-keyword bidding scenarios. These findings represent the first attempt to understand the impact of strategic groups in search advertising and provide interesting implications for advertisers and search engines.This article is published as Nie, Cheng; Zheng, Zhiqiang (Eric); and Sarkar, Sumit (2021) "A Strategic Group Analysis of Competitor Behavior in Search Advertising," Journal of the Association for Information Systems, 22(6), 1659-1685. DOI: 10.17705/1jais.00710
Available at: https://aisel.aisnet.org/jais/vol22/iss6/5. Posted with permission
Performance analysis of the WiNC2R platform:
No full textA Cognitive Radio (CR) is an intelligent transceiver device, able to support multiple technologies, dynamic re-configurability, ease of programming and collaboration with other CR devices to improve the communication efficiency. The two key requirements for an efficient CR implementation are flexibility in operation/programming and speed.
WiNC2R (Winlab Network Centric Cognitive Radio) achieves high speed of operation using its hardware platform and flexibility using its software-configurable architecture. The current WiNC2R architecture implements an 802.11a-like OFDM flow. We evaluate the WiNC2R hardware architecture to see the modularity in the architecture, separation of data and control flow and the performance in terms of latency and throughput. To test the system, the Xilinx Bus Functional Model environment, which is designed to test the IBM standard bus-architecture-based hardware systems, is used. We use a simple ALOHA protocol in the MAC layer to communicate between two WiNC2R nodes and evaluate the performance under the best-case scenario, where the performance is only hindered by the architecture itself rather than external conditions like channel state.
The results of our basic experiments showed that for a single OFDM 802.11a-like flow, the Unit Control Modules (UCM) were idle for almost 80% of the total processing time.
We then tested the WiNC2R system to study the effects of changing the frame size. It was seen that the latencies in the WiNC2R transmitter are frame-size dependent while those in the receiver mainly depend on the size of the data in the last chunk rather than the size of the whole frame. We suggest that chunk size should be 2 OFDM symbols, and chunking be moved to MAC layer for better performance. We give analytical estimates of resulting performance improvement. In the next experiment, we describe virtualization in the WiNC2R by adding more flows. We describe the steps to implement the additional flows and estimate maximum number of concurrent flows possible.
In the last analysis, we show the effect of operating clock frequency on the performance. We prove that at 250 MHz operating frequency and 2 OFDM symbols per chunk, the current WiNC2R implementation will be able to satisfy the SIFS criterion.M.S.Includes bibliographical references (p. 72-73)by Sumit Satarka
Rebellious wives and dysfunctional marriages: Indian women's discourses and participation in the debates over restitution of conjugal rights and the child marriage controversy in the 1880s and 1890s
No full textMonolith and Amkahm
No full textMonolith and Amkahm by #james_hutchinson @as0jhu @meshxuk Mesh showcases work by artists Keith Brown, Annie Cattrell, Bruce Gernand, James Hutchinson, Jon Isherwood, and Sumit Sarkar. The exhibition and commissioned sculptures have been made possible by funding from
Arts Council England @aceagrams #WildinArt.
Location
Gallery Oldham @galleryoldham
Oldham Cultural Quarter
Greaves St
exhibition between:
11th March - 3rd June 2017
10am-5pm Monday-Saturday
For more information visit:
www.meshx.u
Essays on Online Review Manipulation and Sponsored Search Advertising
No full textOnline reviews have been found to be important drivers for consumer’s purchase decisions. Many firms resort to online review manipulations in the hope to improve their revenues. Manipulated online reviews have been acknowledged as a critical challenge in the e-commerce industry. Two essays in my dissertation are in the domain of online review manipulation in the hotel industry. In the first essay, I study how a new type of competition from the sharing economy (specifically Airbnb.com), impacts how hotels manipulate reviews. Surprisingly, I find that hotels demote their competitors less in the presence of higher levels of Airbnb competition. For self-promotion, Airbnb’s impact varies across hotel types – high-end hotels intensify self-promotion activities while low-end hotels make no change. In the second essay, I study the economic effectiveness of different review manipulation strategies. I find that high-end hotels indeed benefit from self-promotion and are hurt from getting demoted by other hotels. Moreover, the negative impact of getting demoted is stronger for low-end hotels than for high-end hotels.
My third essay is in the sponsored search advertising domain. I draw on the theory of strategic groups to investigate how advertisements from firms within such groups impact consumers’ clickstream behavior when viewing sponsored search results. I find a strong positive externality for within-group competitors relative to across-group competitors. My findings contribute to the sponsored search literature by theorizing and empirically verifying consumers’ search behaviors from the strategic group perspective
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