Knowledge that Transforms

Data is becoming an increasingly important asset in today’s organizations. Various challenges in the age of big data, such as high volume and high velocity, call for efficient database maintenance policies. This study focuses on deriving an optimal synchronization policy for consolidated database systems. By trading off synchronization cost with data staleness cost, we propose a time-based dynamic synchronization (TDS) policy, which evaluates the system state at predetermined checkpoints and synchronizes a consolidated database system only if given staleness thresholds are crossed. Although several database synchronization and knowledge refreshment policies have been proposed in the prior literature, the TDS policy retains their advantages and overcomes their inflexibility in that, under the TDS policy, system check and synchronization are easy to schedule, disruptions to business operations can be avoided, and synchronization is run only if necessary. Experimental results show that the TDS policy consistently outperforms benchmark policies, leading to substantial cost savings. In particular, the performance gap between the TDS policy and a static periodic policy is greater when data changes arrive less frequently but carry higher unit staleness costs, or when queries arrive more frequently and suffer higher unit staleness costs.

New technologies, such as virtualization, are transforming the way in which software and services are deployed and delivered to their users. They are behind the emergence of IT offerings such as cloud computing and converged networks, and manifest themselves through two important trends: (1) lower the cost of sharing a common infrastructure across multiple services with disparate resource requirements, and (2) dynamic provisioning of capacity in response to demand. Conventional wisdom is that both of these capabilities are synergistic, with greater provisioning flexibility improving the benefits derived from sharing computing or network resources. Consequently, a service operator should now always favor the use of a shared infrastructure over dedicated solutions when hosting multiple services. In this paper, we ask whether this is indeed the case, and investigate the dual impact of lower costs of sharing and provisioning flexibility on shared and dedicated infrastructures. The investigation reveals that while lower costs are always expected to favor infrastructure sharing, dynamic provisioning plays an ambiguous role. Reprovisioning improves both shared and dedicated solutions, but can do so differently and can sometimes favor a dedicated infrastructure. Our findings help illustrate that the technology trends, such as virtualization, behind cloud computing need not always favor the deployment of services on a shared infrastructure.

Although combinatorial auctions are important mechanisms for many specialized applications, their adoption in general-purpose marketplaces is still fairly limited, partly due to the inherent difficulty in evaluating the efficacy of bids without the availability of comprehensive bidder support. In this paper, we present both theoretical results and computational designs to support real-time feedback to bidders in continuous combinatorial auctions, where bidders are free to join and leave the auction at any time. In particular, we focus on the broad class of single-item multi-unit (SIMU) combinatorial auctions, where multiple identical units of one homogenous item are being auctioned. We also consider two common ways to express bidding preferences: OR bids and XOR bids. For SIMU auctions with each of the two bid types, we present comprehensive analyses of auction dynamics, which can determine winning bids that satisfy allocative fairness, and compute critical evaluative metrics needed to provide bidder support, including bid winning and deadness levels. We also design the data structures and algorithms needed to provide bidder support in real time for SIMU auctions of practically relevant sizes. The computational tools proposed in this paper can facilitate the efficient and more transparent implementation of SIMU combinatorial auctions in business- and consumer-oriented markets.

There is substantial knowledge about how individuals and organizations, which we refer to collectively as entities, adopt and use new channels. However, less is known about how this relates to their use of the incumbent channel that the new channel may replace. To address this gap, we examine how entities transition between incumbent and new channels over time, with a particular focus on two temporal factors: when an entity adopts the new channel and how long an entity has used the channels, which we refer to as the entity’s channel history. Our results show that entities that adopt at similar times often follow dramatically different patterns of new and incumbent channel use. This allows us to expand upon the traditional adopter categories of innovators, early adopters, early majority, late majority, and laggards. We also find that an entity’s channel history influences how it transitions between the incumbent and new channels, and we document other factors that influence these transitions. Our results contribute to theory about the adoption/diffusion of new channels, and they contribute to practice by giving managers tools to understand and predict how entities’ use of new and incumbent channels evolves over time.

Many papers in the literature have analyzed the role search rank plays in influencing user choices for the same product offered through different links. However, the literature has not analyzed whether search position can cause users to change their consumption between two ex ante distinct product categories. We analyze this question in the context of the ongoing public policy debate surrounding the role search engines can play in anti-piracy efforts. Specifically, we analyze whether reducing the prominence of infringing links can impact users choices between legal and infringing content. To do this we design a customized search engine that allows us to manipulate the positions of infringing and legal links in users’ search results. We then use this search engine to conduct experiments on a general population of users and on a subset of college-aged users. Our data show that reducing the prominence of infringing links in search results causes users who otherwise would have consumed infringing content to switch their consumption to paid legal content, and that these results hold even among users whose initial search queries express an explicit preference for infringing content. These results suggest that even small changes in the cost of discovering pirated content can have a relatively large impact on user behavior. As such, our results inform an important public policy debate by showing that search engines have a vital role to play in the fight against online copyright theft.

Code reuse is fundamental to the development of open-source software (OSS). Therefore, understanding how and why it occurs is important. To date, researchers have examined code reuse in OSS largely from the perspective of leaders. We show why followers must be considered as well. “Followers” are people who have had previous contacts with an individual from another project and who continue to associate with him or her. We consider two types of followers: developers (those directly involved in software development) and observers (those indirectly involved in it). We conduct a series of empirical investigations by using a longi-tudinal dataset of OSS projects hosted in GitHub, along with a survey and qualitative data. We find that followership can affect code reuse, but the effect depends on the nature of the follower (developer or observer). Overall, our study suggests that followership is important for code reuse in OSS because it enables participants to learn, and learning promotes code reuse.