DYNAMIC FILE MIGRATION IN DISTRIBUTED COMPUTER SYSTEMS

In a distributed computer system files are shared by both local users and remote users for query and update purposes. A user performing data processing activities tends to reference the same file for some time. When the referenced file is stored remotely, large amounts of communication traffic will be generated. For example, when a customer is making a travel plan, an airline reservation database might be accessed repeatedly by a remote operation site. The inquiries will probably all be made within the time of an ordinary telephone conversation. In many recent developments in distributed computer systems, file migration operations are incorporated into the procedures for processing remote file access requests. Using file migration operations a file may be duplicated or moved to the requesting site in order to reduce communication traffic. As a result, the system is faced with dynamic file placement decisions using a file migration policy. In particular, a file migration policy is expressed as the IF-THEN rules that specify the file migration operations to be implemented at each viable system state. Based on this policy, file migration operations are triggered when the specified conditions are satisfied, and thus dynamically respond to system needs. Because of the dynamic behaviors of systems, the problem of deriving effective file migration policies is extremely complex. An elaborate analysis is required. This paper studies the impact of file migration operations on system performance and develops automatic mechanisms for incorporating file migrations as part of system operations. The mechanisms include optimization models formulated in the form of Markov decision models for deriving optimal file migration policies at system design or redesign points, and heuristic rules to generate adaptive file migration decisions for individual file access requests. The trade-off between these two types of mechanisms is clearly that of performance levels versus implementation complexities. The optimization analysis not only generates the best possible solutions, but provides insight into the problem structure, whereas the rationale for developing heuristics is their simplicity in implementation and acceptable performance levels

Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement

This study attempts to discover and evaluate the predictive power of stock micro blog sentiment on future stock price directional movements. We construct a set of robust models based on sentiment analysis and data mining algorithms. Using 72,221 micro blog postings for 1909 stock tickers and 3874 distinct authors, our study reveals not only that stock micro blog sentiments do have predictive power for simple and market-adjusted returns respectively, but also that this predictive accuracy is consistent with the underreaction hypothesis observed in behavioral finance. We establish that stock micro blog with its succinctness, high volume and real-time features do have predictive power over future stock price movements. Furthermore, this study provides support for the model of irrational investor sentiment, recommends a supplementary investing approach using user-generated content and validates an instrument that may contribute to the monetization schemes for Virtual Investing Communities

Avoiding the Blind Spots: Competitor Identification Using Web Text and Linkage Structure

The importance of identifying competitors and of avoiding “competitive blind spots” in marketplace has been well emphasized in research and practice. However, identification of competitors is non-trivial and requires active monitoring of a focal company\u27s competitive environment. The difficulty in such identification is amplified manifold when there are many more than one focal company of interest. As the web presence of companies, their clients/consumers, and their suppliers continues to grow, it is increasingly realistic to assume that the real-world competitive relationships are reflected in the text and linkage structure of the relevant pages on the web. However, finding the appropriate web-based cues that effectively signal competitor relationships remains a challenge. Using web data collected for more than 2500 companies of the Russell 3000 index, we explore the notion that web cues can allow us to discriminate, in a statistically significant manner, between competitors and non-competitors. Based on this analysis, we present an automated technique that uses the most significant web-based cues and applies predictive modeling to identify competitors. We find that several web-based metrics on an average have significantly different values for companies that are competitors as opposed to noncompetitors. We also find that the predictive models built using web-based metrics that we suggest provide high precision, recall, F measure, and accuracy in identifying competitors

The Causal Impact of Fit Valence and Fit Reference on Online Product Returns

We investigate the causal impact of two types of product fit-related information – fit valence and fit reference – on online product return rate by leveraging a change in the product review system that took place at an online retailer. This quasi-experiment in the apparel product category allows us to examine the importance of fit information. We find that the mere presentation of either fit-valence (e.g. “true to size”) or fit-reference information (e.g. body size) by itself does not help reduce purchase errors. Rather, it is the combination of the two types of fit information in a review that drives the drop in product return rate. We employ the lens of semantic relativism to illustrate how customers interpret fit-valence expressions by using the fit-reference information provided by the same reviewer. Our findings offer useful business implications to online retailers grappling with high product return rates for merchandise where fit matters