Global Semantic Integrity Constraint Checking for a System of Databases

In today’s emerging information systems, it is natural to have data distributed across multiple sites. We define a System of Databases (SyDb) as a collection of autonomous and heterogeneous databases. R-SyDb (System of Relational Databases) is a restricted form of SyDb, referring to a collection of relational databases, which are independent. Similarly, X-SyDb (System of XML Databases) refers to a collection of XML databases. Global integrity constraints ensure integrity and consistency of data spanning multiple databases. In this dissertation, we present (i) Constraint Checker, a general framework of a mobile agent based approach for checking global constraints on R-SyDb, and (ii) XConstraint Checker, a general framework for checking global XML constraints on X-SyDb. Furthermore, we formalize multiple efficient algorithms for varying semantic integrity constraints involving both arithmetic and aggregate predicates. The algorithms take as input an update statement, list of all global semantic integrity constraints with arithmetic predicates or aggregate predicates and outputs sub-constraints to be executed on remote sites. The algorithms are efficient since (i) constraint check is carried out at compile time, i.e. before executing update statement; hence we save time and resources by avoiding rollbacks, and (ii) the implementation exploits parallelism. We have also implemented a prototype of systems and algorithms for both R-SyDb and X-SyDb. We also present performance evaluations of the system

A Personalized Travel Recommendation System Using Social Media Analysis

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model

A Methodology for Engineering Collaborative and ad-hoc Mobile Applications using SyD Middleware

Today’s web applications are more collaborative and utilize standard and ubiquitous Internet protocols. We have earlier developed System on Mobile Devices (SyD) middleware to rapidly develop and deploy collaborative applications over heterogeneous and possibly mobile devices hosting web objects. In this paper, we present the software engineering methodology for developing SyD-enabled web applications and illustrate it through a case study on two representative applications: (i) a calendar of meeting application, which is a collaborative application and (ii) a travel application which is an ad-hoc collaborative application. SyD-enabled web objects allow us to create a collaborative application rapidly with limited coding effort. In this case study, the modular software architecture allowed us to hide the inherent heterogeneity among devices, data stores, and networks by presenting a uniform and persistent object view of mobile objects interacting through XML/SOAP requests and responses. The performance results we obtained show that the application scales well as we increase the group size and adapts well within the constraints of mobile devices

A framework for caching relevant data items for checking integrity constraints of mobile database

In a mobile environment, due to the various constraints inherited from limitations of wireless communication and mobile devices, checking for integrity constraints to maintain the consistent state of mobile databases is an important issue that needs to be addressed. Hence, in this paper we propose a framework for caching relevant data items needed during the process of checking integrity constraints of mobile databases. This is achieved by analyzing the relationships among the integrity tests (simplified form of integrity constraints) to be evaluated for a given update operation. This improves the checking mechanism by preventing delays during the process of checking constraints and performing the update. Hence, our model speeds up the checking process

Social Media and Forecasting Stock Price Change

The Stock Market is a big influence on both national and international economies. Stock prices are driven by a number of factors: industry performance, company news and performance, investor confidence, micro and macro economic factors like employment rates, wage rates, etc. Stock pricing trends can be gauged from the factors that drive it as well as from the stock\u27s historical performance. As fluctuations in stock prices become more volatile and unpredictable, forecasting models help reduce some of the randomness involved in investing and financial decision making. Users on social media platforms like twitter, StockTwits, and eToro discuss issues related to the stock market. Can the analysis of posts on StockTwits add value to the existing features of stock price predicting models? An existing model that uses twits as features was extended to include sentiment analysis of the text referenced by the URL in the twits to see if the model accuracy did improve. Initial results indicate that the addition of sentiment analysis of the text referenced by the URL does not improve the performance of the model when all twits for a given day are taken into account since the model only identifies the direction of change and not the degree of change. The stock prediction model achieves 65% accuracy compared to the base case accuracy of 44% and augmenting the model with sentiment analysis did not change the accuracy. The study highlights some interesting observations regarding users on the StockTwits social media platform and proposes the need for a domain specific sentiment analyzer in future work

A framework for caching relevant data divisions for checking integrity constraints of mobile databases

In a mobile environment, due to the various constraints inherited from limitations of wireless communication and mobile devices, checking for integrity constraints to maintain the consistent state of mobile databases is an important issue that needs to be addressed. Hence, in this paper we propose a framework for caching relevant data items needed during the process of checking integrity constraints of mobile databases. This is achieved by analyzing the relationships among the integrity tests (simplified form of integrity constraints) to be evaluated for a given update operation. This improves the checking mechanism by preventing delays during the process of checking constraints and performing the update. Hence, our model speeds up the checking process

Towards Clinical Decision Support for Veteran Mental Health Crisis Events using Tree Algorithm

This research focuses on establishing a psychological treatment system especially for Milwaukee based veterans outside the traditional clinical environment of Veterans Affairs (VA). As part of this process, a 12- week intervention had been made. Data had been collected related to different health aspects and psychological measurements. With the help of expert veterans and psychologist, we had defined early warning signs, acute crisis and long-term crisis from this dataset. We had used different algorithms to predict long term crisis using acute crisis and early warning signs. At the end, we had established a clinical decision-making rule to assist peer mentor veterans to help their fellow mentee veterans especially those suffering from PTSD

SyD: A Middleware Testbed for Collaborative Applications over Small Heterogeneous Devices and Data Stores

Abstract. Currently, it is possible to develop a collaborative application running on a collection of heterogeneous, possibly mobile, devices, each potentially hosting data stores, using existing middleware technologies such as JXTA, BREW, compact.NET and J2ME. However, they require too many ad-hoc techniques as well as cumbersome and time-consuming programming. Our System on Mobile Devices (SyD) middleware, on the other hand, has a modular architecture that makes such application de-velopment very systematic and streamlined. The architecture supports transactions over mobile data stores, with a range of remote group invo-cation options and embedded interdependencies among such data store objects. The architecture further provides a persistent uniform object view, group transaction with Quality of Service (QoS) speci¯cations, and XML vocabulary for inter-device communication. This paper presents the basic SyD concepts, introduces the architecture and the design of the SyD middleware and its components. We also provide guidelines fo