A key takeaway from the COVID-19 crisis is the need for scalable methods and systems for ingestion of big data related to the disease, such as models of the virus, health surveys, and social data, and the ability to integrate and analyze the ingested data rapidly. One specific example is the use of the Internet of Things and wearables (i.e., the Oura ring) to collect large-scale individualized data (e.g., temperature and heart rate) continuously and to create personalized baselines for detection of disease symptoms. Individualized data, when collected, has great potential to be linked with other datasets making it possible to combine individual and societal scale models for further understanding the disease. However, the volume and variability of such data require novel big data approaches to be developed as infrastructure for scalable use. This paper presents the data pipeline and big data infrastructure for the TemPredict project, which, to the best of our knowledge, is the largest public effort to gather continuous physiological data for time-series analysis. This effort unifies data ingestion with the development of a novel end-to-end cyberinfrastructure to enable the curation, cleaning, alignment, sketching, and passing of the data, in a secure manner, by the researchers making use of the ingested data for their COVID-19 detection algorithm development efforts. We present the challenges, the closed-loop data pipelines, and the secure infrastructure to support the development of time-sensitive algorithms for alerting individuals based on physiological predictors illness, enabling early intervention.

The growth in network capacities and availability has been accompanied by a proliferation of online games. While online games perform well under many network conditions, Internet delays can often degrade online game performance. The precise impact that latency has on online gameplay depends upon the game type and the actions within the game. While the effects of latency on specific games has been studied, knowledge about the effects of latency on classes of games and about the effects of latency on different player actions is lacking. This paper presents a broad, perspective-based classification of games based on player control and camera view. The foundation of the game classification are player actions, each of which is defined by its precision and deadline requirements. Experiments with controlled amount of precision, deadline and latency support the classification. This classification of games should prove useful for game designers, network engineers and game players themselves as they build and play on tomorrow's networks.

Hardware and platform limitations restrict the display settings for most computer games, forcing a tradeoff between frame rate and resolution to achieve acceptable performance. Previous work has explored the effects of frame rate and/or resolution on a variety of multimedia applications, but most of these are less interactive than typical computer games. Previous work within the context of computer games has concentrated primarily on user actions for specific environments, such as combat in a first-person shooter game. This paper provides a detailed study of the effects of frame rate and resolution on discrete, canonical actions common to many games, shooting and navigation. The study uses a novel perspective based classification defined by the position of the camera relative to the user and the visual change in object sizes relative to the camera, to further refine the findings across a broad spectrum of game genres. A custom game with levels that combine actions and perspectives and measures user performance with different display settings provides the core for the user study experiments. Analysis for over 25 users shows that frame rate has a much greater impact on user performance than does resolution across all game perspectives and gameplay actions. Both frame rate and resolution impact user opinion on playability and quality. These insights into the effects of frame rates and resolution on user performance and opinions can guide game players in their choice for game settings and new hardware purchases, and inform system designers in their development of new hardware.

Complex event processing has become increasingly important in modern applications, ranging from RFID tracking for supply chain management to real-time intrusion detection. A key aspect of complex event processing is to extract patterns from event streams to make informed decisions in real-time. However, network latencies and machine failures may cause events to arrive out-of-order at the event processing engine. State-of-the-art event stream processing technology experiences significant challenges when faced with out-of-order data arrival including output blocking, huge system latencies, memory resource overflow, and incorrect result generation. To address these problems, we propose two alternate solutions: aggressive and conservative strategies respectively to process sequence pattern queries on out-of-order event streams. The aggressive strategy produces maximal output under the optimistic assumption that out-of-order event arrival is rare. In contrast, to tackle the unexpected occurrence of an out-of-order event and with it any premature erroneous result generation, appropriate error compensation methods are designed for the aggressive strategy. The conservative method works under the assumption that out-of-order data may be common, and thus produces output only when its correctness can be guaranteed. A partial order guarantee (POG) model is proposed under which such correctness can be guaranteed. For robustness under spiky workloads, both strategies are supplemented with persistent storage support and customized access policies. Our experimental study evaluates the robustness of each method, and compares their respective scope of applicability with state-of-art methods.

This demonstration presents a complex event processing system which focuses on out-of-order handling. State-of-the-art event stream processing technology experiences significant challenges when faced with out-of-order data arrival including huge system latencies, missing results, and incorrect result generation. We propose two out-of-order handling techniques, conservative and aggressive strategies. We will show the efficiency of our techniques and how they can satisfy various QoS requirements of different applications.

It is common practice for software developers to use examples to guide development efforts. This largely unwritten, yet standard, practice of "develop by example" is often supported by examples bundled with library or framework packages, provided in textbooks, and made available for download on both official and unofficial web sites. However, the vast number of examples that are embedded in the billions of lines of already developed library and framework code are largely untapped. We have developed XSnippet, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand. In particular, our work makes three primary contributions. First, a range of queries is provided to allow developers to switch between a context-independent retrieval of code snippets to various degrees of context-sensitive retrieval for object instantiation queries. Second, a novel graph-based code mining algorithm is provided to support the range of queries and enable mining within and across method boundaries. Third, an innovative context-sensitive ranking heuristic is provided that has been experimentally proven to provide better ranking for best-fit code snippets than context-independent heuristics such as shortest path and frequency. Our experimental evaluation has shown that XSnippet has significant potential to assist developers, and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.

Multiple sequence alignment represents a class of powerful bioinformatics tools with many uses in computational biology ranging from discovery of characteristic motifs and conserved regions in protein families to improved prediction of secondary and tertiary structure. Today, with rapidly growing data repositories offering scientists significantly more data with which to make better decisions, it is increasingly important to run these multiple alignment calculations as rapidly as possible. However, while several multiple alignment algorithms have been developed, these algorithms remain computationally expensive taking as long as 2 to 3 days for some queries. In this paper, we propose a new caching technique to improve the performance of multiple sequence alignment algorithms. In particular, we propose a nested two level cache hierarchy that provides caching of pairwise alignment results - a computationally expensive subcomponent of the multiple sequence alignment algorithms. A key contribution of our work is the development of two novel cache replacement policies that closely track the scientist’s query patterns over time. We present experimental results that validate the benefits of caching over the repeated computation of the alignments, provide heuristics for determining which alignments would benefit from the caching, and show the effectiveness of the developed cache replacement policies.

Many projects currently used in Software Engineering curricula lack both the “fun factor ” needed to engage students, as well as the practical realism of engineering projects that include other computer science disciplines such as Software Engineering, Networks, or Human Computer Interaction. This paper reports on our endeavor to enhance interest and retention in an existing Software Engineering curriculum through the use of computer game-based projects. Specifically, a set of game-centric, project-based modules have been developed that enable students to: (1) actively participate in the different phases of the software lifecycle taking a single project from requirement elicitation to testing and maintenance; (2) expose students to real issues in project and team management over the course of a 2-semester project; and at the same time (3) introduce students to the different aspects of computer game design. Preliminary results suggest the merits of our approach, showing improved class participation and performance.

Integration of multiple heterogeneous data sources continues to be a critical problem for many application domains and a challenge for researchers world-wide. One aspect of integration is the translation of schema and data across data model boundaries. Researchers in the past have looked at both customized algorithmic approaches as well as generic meta-modeling approaches as viable solutions. We now take the meta-modeling approach the next step-forward. In this paper we propose a flexible, extensible and re-usable transformation modeling framework which allows users to (1) model their transformations; (2) to choose from a set of possible execution strategies to translate the underlying schema and data; and (3) to access and re-use a library of transformation generators. In this paper we present the core of our modeling framework - a set of cross algebra operators that covers the class of linear transformations, and two different techniques of composing these operators into larger transformation expressions. We also present an evaluation strategy to execute the modeled transformation, and thereby transform the input schema and data into the target schema and data assuming that data model wrappers are provided for each data model. The proposed framework has been implemented, and we give an overview of this prototype system.

No abstract available.

No abstract available.

Article SERF: schema evolution through an extensible, re-usable and flexible framework Share on Authors: Kajal T. Claypool Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA Department of Computer Science, Worcester Polytechnic Institute, Worcester, MAView Profile , Jing Jin Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA Department of Computer Science, Worcester Polytechnic Institute, Worcester, MAView Profile , Elke A. Rundensteiner Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA Department of Computer Science, Worcester Polytechnic Institute, Worcester, MAView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 314–321https://doi.org/10.1145/288627.288672Online:01 November 1998Publication History 39citation359DownloadsMetricsTotal Citations39Total Downloads359Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access