We present KnIT, the Knowledge Integration Toolkit, a system for accelerating scientific discovery and predicting previously unknown protein-protein interactions. Such predictions enrich biological research and are pertinent to drug discovery and the understanding of disease. Unlike a prior study, KnIT is now fully automated and demonstrably scalable. It extracts information from the scientific literature, automatically identifying direct and indirect references to protein interactions, which is knowledge that can be represented in network form. It then reasons over this network with techniques such as matrix factorization and graph diffusion to predict new, previously unknown interactions. The accuracy and scope of KnIT's knowledge extractions are validated using comparisons to structured, manually curated data sources as well as by performing retrospective studies that predict subsequent literature discoveries using literature available prior to a given date. The KnIT methodology is a step towards automated hypothesis generation from text, with potential application to other scientific domains.

Keeping up with the ever-expanding flow of data and publications is untenable and poses a fundamental bottleneck to scientific progress. Current search technologies typically find many relevant documents, but they do not extract and organize the information content of these documents or suggest new scientific hypotheses based on this organized content. We present an initial case study on KnIT, a prototype system that mines the information contained in the scientific literature, represents it explicitly in a queriable network, and then further reasons upon these data to generate novel and experimentally testable hypotheses. KnIT combines entity detection with neighbor-text feature analysis and with graph-based diffusion of information to identify potential new properties of entities that are strongly implied by existing relationships. We discuss a successful application of our approach that mines the published literature to identify new protein kinases that phosphorylate the protein tumor suppressor p53. Retrospective analysis demonstrates the accuracy of this approach and ongoing laboratory experiments suggest that kinases identified by our system may indeed phosphorylate p53. These results establish proof of principle for automated hypothesis generation and discovery based on text mining of the scientific literature.

Identifying drug-drug interactions is an important and challenging problem in computational biology and healthcare research. There are accurate, structured but limited domain knowledge and noisy, unstructured but abundant textual information available for building predictive models. The difficulty lies in mining the true patterns embedded in text data and developing efficient and effective ways to combine heterogenous types of information. We demonstrate a novel approach of leveraging augmented text-mining features to build a logistic regression model with improved prediction performance (in terms of discrimination and calibration). Our model based on synthesized features significantly outperforms the model trained with only structured features (AUC: 96% vs. 91%, Sensitivity: 90% vs. 82% and Specificity: 88% vs. 81%). Along with the quantitative results, we also show learned "latent topics", an intermediary result of our text mining module, and discuss their implications.

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Text and images are two major sources of information in scientific literature. Information from these two media typically reinforce and complement each other, thus simplifying the process for human to extract and comprehend information. However, machines cannot create the links or have the semantic understanding between images and text. We propose to integrate text analysis and image processing techniques to bridge the gap between the two media, and discover knowledge from the combined information sources, which would be otherwise lost by traditional single-media based mining systems. The focus is on the chemical entity extraction task because images are well known to add value to the textual content in chemical literature. Annotation of US chemical patent documents demonstrates the effectiveness of our proposal.

Patents are of crucial importance for businesses, because they provide legal protection for the invented techniques, processes or products. A patent can be held for up to 20 years. However, large maintenance fees need to be paid to keep it enforceable. If the patent is deemed not valuable, the owner may decide to abandon it by stopping paying the maintenance fees to reduce the cost. For large companies or organizations, making such decisions is difficult because too many patents need to be investigated. In this paper, we introduce the new patent mining problem of automatic patent maintenance prediction, and propose a systematic solution to analyze patents for recommending patent maintenance decision. We model the patents as a heterogeneous time-evolving information network and propose new patent features to build model for a ranked prediction on whether to maintain or abandon a patent. In addition, a network-based refinement approach is proposed to further improve the performance. We have conducted experiments on the large scale United States Patent and Trademark Office (USPTO) database which contains over four million granted patents. The results show that our technique can achieve high performance.

The emergence of new social media such as blogs, message boards, news, and web content in general has dramatically changed the ecosystems of corporations. Consumers, non-profit organizations, and other forms of communities are extremely vocal about t

Corporations are extremely sensitive to issues such as brand stewardship and product reputation. Traditional brand image and reputation tracking is limited to news wires and contact centre analysis. However, with the emergence of web, Consumer Genera

The emergence of new social media such as blogs, message boards, news, and Web content in general has dramatically changed the ecosystems of corporations. Consumers, non-profit organizations, and other forms of communities are extremely vocal about their opinions and perceptions on companies and their brands on the Web. The ability to leverage such "voice of the Web" to gain consumer, brand, and market insights can be truly differentiating and valuable to todaypsilas corporations. In particular, one important form of insights can be derived from sentiment analysis on Web content. Sentiment analysis traditionally emphasizes on classification of Web comments into positive, neutral, and negative categories. This paper goes beyond sentiment classification by focusing on techniques that could detect the topics that are highly correlated with the positive and negative opinions. Such techniques, when coupled with sentiment classification, can help the business analysts to understand both the overall sentiment scope as well as the drivers behind the sentiment. In this paper, we describe our overall sentiment analysis system that consists of such sentiment analysis techniques. We then detail a novel topic detection method using point-wise mutual information and term frequency distribution. We demonstrate the effectiveness of our overall approaches via several case studies on different social media data sets.

A strong brand is a major asset to any corporation. Traditional brand image and reputation tracking is limited to news wires and contact centers analysis. However, with the emergence of Web, consumer generated media (CGM), such as blogs, news forums, message boards, and web pages/sites, is rapidly transforming the way companies analyze their brand perceptions. This paper describes the next generation COBRA (Corporate Brand and Reputation Analysis) approach to mining a wide range of CGM content to discover how the social media-based community perceives the brand. The solution processes a diverse set of structured and unstructured information and mines CGM content by generating multiple taxonomies from the data. These taxonomies are then used singly and in combination to better understand important brand characteristics and hence enhance marketing and strategic decision making. We illustrate our approach with a real-world case study involving the Kraft Foodspsila Vegemite brand.

Rapidly leveraging information analytics technologies to mine the mounting information in structured and unstructured forms, derive business insights and improve decision making is becoming increasingly critical to today's business successes. One of the key enablers of the analytics technologies is an Information Warehouse Management System (IWMS) that processes different types and forms of information, builds, and maintains the information warehouse (IW) effectively. Although traditional multi-dimensional data warehousing techniques, coupled with the well-known ETL processes (Extract, Transform, Load) may meet some of the requirements in an IWMS, in general, they fall short on several major aspects: 1. They often lack comprehensive support for both structured and unstructured data processing; 2. they are database-centric and require detailed database and data warehouse knowledge to perform IWMS tasks, and hence they are tedious and time-consuming to operate and learn; 3. they are often inflexible and insufficient in coping with a wide variety of on-going IW maintenance tasks, such as adding new dimensions and handling regular and lengthy data updates with potential failures and errors.

Corporations are extremely sensitive to issues such as brand stewardship and product reputation. Traditional brand image and reputation tracking is limited to news wires and contact centres analysis. However, with the emergence of Web, consumer generated media (COM), such as blogs, news forums, message boards, and Web pages/sites, is rapidly becoming the "voice of the people". This paper describes a COBRA (corporate brand and reputation analysis) solution that mines a wide range of COM contents for brand and reputation analysis. The solution contains a flexible ETL (Extract, Transform, and Load) engine that processes diverse sets of structured and unstructured information, a suite of analytical capabilities that mines COM content to extract semantic entities and insights out of the data, and an alerting mechanism that utilizes the analytics results to accurately generate brand and reputation alerts. We use a real-world case study to demonstrate the effectiveness of our approach.

Parallel I/O systems typically consist of individual processors, communication networks, and a large number of disks. Managing and utilizing these resources to meet performance, portability, and usability goals of high performance scientific applications has become a significant challenge. For scientists, the problem is exacerbated by the need to retune the I/O portion of their code for each supercomputer platform where they obtain access. We believe that a parallel I/O system that automatically selects efficient I/O plans for user applications is a solution to this problem. The authors present such an approach for scientific applications performing collective I/O requests on multidimensional arrays. Under our approach, an optimization engine in a parallel I/O system selects high quality I/O plans without human intervention, based on a description of the application I/O requests and the system configuration. To validate our hypothesis, we have built an optimizer that uses rule based and randomized search based algorithms to tune parameter settings in Panda, a parallel I/O library for multidimensional arrays. Our performance results obtained from an IBM SP using an out-of-core matrix multiplication application show that the Panda optimizer is able to select high quality I/O plans and deliver high performance under a variety of system configurations with a small total optimization overhead.

Article Parallel I/O for scientific applications on heterogeneous clusters: a resource-utilization approach Share on Authors: Yong E. Cho Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, IL Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, ILView Profile , Marianne Winslett Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, IL Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, ILView Profile , Szu-wen Kuo Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, IL Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, ILView Profile , Jonghyun Lee Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, IL Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, ILView Profile , Ying Chen IBM Almaden Research Center and Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, IL IBM Almaden Research Center and Department of Computer Science, University of Illinois, 1304 W. Springfield, Urbana, ILView Profile Authors Info & Claims ICS '99: Proceedings of the 13th international conference on SupercomputingJune 1999 Pages 253–259https://doi.org/10.1145/305138.305200Published:01 May 1999 10citation304DownloadsMetricsTotal Citations10Total Downloads304Last 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

The complexity of parallel I/O systems imposes significant challenge in managing and utilizing the available system resources to meet application performance, portability and usability goals. We believe that a parallel I/O system that automatically selects efficient I/O plans for user applications is a solution to this problem. We present such an automatic performance optimization approach for scientific applications performing collective I/O requests on multidimensional arrays. The approach is based on a high level description of the target workload and execution environment characteristics, and applies genetic algorithms to select high quality I/O plans. We have validated this approach in the Panda, parallel I/O library. Our performance evaluations on the IBM SP show that this approach can select high quality I/O plans under a variety of system conditions with a low overhead, and the genetic algorithm-selected I/O plans are in general better than the default plans used in Panda.

Fine grained data distributions are widely used to balance computational loads across compute processes in parallel scientific applications. When a fine grained data distribution is used in memory, performance of I/O intensive applications can be limited not only by disk speed but also by message passing, because a large number of small messages may be generated by the implementation strategy used in the underlying parallel file system or parallel I/O library. Combining (or packetizing) a set of small messages into a large message is generally known to speed up parallel I/O. However, overall I/O performance is affected not only by small messages but also by other factors like cyclic block size and interconnect characteristics. We describe small message combination and communication scheduling for fine grained data distributions in the Panda parallel I/O library and analyze I/O performance on parallel platforms having different interconnects: IBM SP2, IBM workstation cluster connected by FDDI and Pentium II cluster connected by Myrinet.

Parallel I/O systems typically consist of individual processors, communication networks, and a large number of disks. Managing and utilizing these resources to meet performance, portability and usability goals of applications has become a significant challenge. We believe that a parallel I/O system that automatically selects efficient I/O plans for user applications is a solution to this problem. In this paper, we present such an automatic performance optimization approach for scientific applications performing collective I/O requests on multidimensional arrays. Under our approach, an optimization engine in a parallel I/O system selects optimal I/O plans automatically without human intervention based on a description of the application I/O requests and the system configuration. To validate our hypothesis, we have built an optimizer that uses a rule-based and randomized search-based algorithms to select optimal parameter settings in Panda, a parallel I/O library for multidimensional arr...

Adaptive mesh refinement (AMR) is a promising computational approach that investigates a scientific problem using grids of different resolutions in different portions of the problem space. This allows the more "interesting" areas of the problem space to be investigated in more detail than other areas. We present efficient ways for AMR-type applications to do input/output on the IBM SP2. We propose a disk layout that minimizes I/O costs for checkpoint/restart operations and investigate several allocation approaches to decluster grid data to multiple disks to enhance I/O parallelism for timestep/visualization operations. The experiments on the SP2 show that our strategies can help to shorten the I/O time to meet the performance requirements of the two types of I/O operations.

While individual parallel I/O systems can incorporate sophisticated techniques and achieve impressive performance in particular situations, researchers as yet have only limited m~derstanding of the impact of various design decisions or of the techniques required for performance robustness.One remedy is to perform detailed comparative studies of different I/O libraries.In this paper, we describe such a study for the Disk Resident Array and Panda libraries, both designed to support high-performance I/O for arrays.While the two systems have many similarities, their designs and implementations are based on different assumptions and target different applications.We base our study on two I/O structures commonly encountered in scientific applications: the collective read/write of an entire array and the collective read/write of an arbitrary array section.Experiments are performed on two parallel file systems (IBM PIOFS and Intel PFS) antI one commodity Unix file system (AIX JFS).Our results yield insights into

Panda is a high performance library for accessing large multidimensional array data on secondary storage of parallel platforms and networks of workstations. When using Panda as the I/O component of a scientific application, H3expresso, on the IBM SP2 at Cornell Theory Center, we found that some nodes are more powerful with respect to I/O than others, requiring the introduction of load balancing techniques to maintain high performance. We expect that heterogeneity will also be a big issue for DBMSs or parallel I/O libraries designed for scientific applications running on networks of workstations, and the methods of allocating data to servers in these environments will need to be upgraded to take heterogeneity into account, while still allowing users to exert control over data layout. We propose such an approach to load balancing, under which we respect the user's choice of high level disk layout, but introduce automatic subchunking. The use of subchunks allows us to divide the very large chunks typically specified by the user's disk layout into more manageable size units that can be allocated to I/O nodes in a manner that fairly distributes the load. We also present two techniques for allocating subchunks to nodes, static and dynamic, and evaluate their performance on the SP2.

We present an analytical performance model for Panda, a library for synchronized i/o of large multidimensional arrays on parallel and sequential platforms, and show how the Panda developers use this model to evaluate Panda's parallel i/o performance and guide future Panda development. The model validation shows that system developers can simplify performance analysis, identify potential performance bottlenecks, and study the design trade-offs for Panda on massively parallel platforms more easily than by conducting empirical experiments. More importantly, we show that the outputs of the performance model can be used to help make optimal plans for handling application i/o requests, the first step toward our long-term goal of automatically optimizing i/o request handling in Panda.

Large multidimensional arrays are a common data type in high-performance scientific applications. Without special techniques for handling access to these arrays, I/O can easily become a large fraction of execution time for applications using these arrays, especially on parallel platforms. We show how to reduce the parallel I/O bottleneck for array data in closely-synchronized SPMD applications on distributed-memory platforms, through the use of server-directed I/O. This method allows array data requests on parallel platforms to be translated into long sequential disk reads and writes, while also minimizing the cost of rearranging data as they move between on-disk and in-memory schemas. We present experimental results from the implementation of server-directed I/O in Panda, showing that for I/O of large arrays, Panda utilizes nearly the maximum throughput of the underlying AIX file system on an IBM SP2. We also discuss Panda's user interface, an essential factor in Panda's high performance.

We present the architecture and implementation results for Panda 2.0, a library for input and output of multidimensional arrays on parallel and sequential platforms. Panda achieves remarkable performance levels on the IBM SP2, showing excellent scalability as data size increases and as the number of nodes increases, and provides throughputs close to the full capacity of the AIX file system on the SP2 we used. We argue that this good performance can be traced to Panda's use of server-directed i/o (a logical-level version of disk-directed i/o [Kotz94b]) to perform array i/o using sequential disk reads and writes, a very high level interface for collective i/o requests, and built-in facilities for arbitrary rearrangements of arrays during i/o. Other advantages of Panda's approach are ease of use, easy application portability, and a reliance on commodity system software.