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In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e.,B,I,Oonly) compared to multiple entity-classes in traditional NER (i.e.,Bentity1,Bentity2,Ientity1,I,O), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

Identifying a patient’s important medical problems requires broad and deep medical expertise, as well as significant time to gather all the relevant facts from the patient’s medical record and assess the clinical importance of the facts in reaching the final conclusion. A patient’s medical problem list is by far the most critical information that a physician uses in treatment and care of a patient. In spite of its critical role, its curation, manual or automated, has been an unmet need in clinical practice. We developed a machine learning technique in IBM Watson to automatically generate a patient’s medical problem list. The machine learning model uses lexical and medical features extracted from a patient’s record using NLP techniques. We show that the automated method achieves 70% recall and 67% precision based on the gold standard that medical experts created on a set of deidentified patient records from a major hospital system in the US. To the best of our knowledge this is the first successful machine learning/NLP method of extracting an open-ended patient’s medical problems from an Electronic Medical Record (EMR). This paper also contributes a methodology for assessing accuracy of a medical problem list generation technique.

As the use of Electronic Medical Records (EMRs) becomes widespread, the amount of data in an EMR becomes a challenge for its comprehension. We developed problem-oriented EMR summarization to address this issue, as a part of a larger effort of adapting IBM Watson to the medical domain. The problem-orientation refers to the central role of a patient's medical problems in the summary. The summarization uses a generated problem list, relates these generated medical problems to relevant clinical data, and organizes the clinical data in a medically meaningful manner. Watson analytics are used for creating the summarization. This is a step in building the next generation EMR, one that is based not on just keeping record but instead on a conceptual understanding of medicine, thereby crossing the threshold from record storage to an intelligent entity for clinical decision making.

This paper presents a new, compelling argument for a larger potential to reduce labor cost by adopting cloud computing in enterprise IT. The additional cost reduction is possible in the labor intensive activities of customer-specific customization of cloud services, tools support, and customer-facing service management. These essential service delivery processes are excellent candidates for standardization and automation using the cloud computing principles. We present examples of such processes and potential for optimization in these processes.

In a typical receiver, symbol synchronization must be established before symbol demodulation. This paper discusses synchronization for an isolated symbol from a filter-bank type multicarrier system. It is shown that accurate symbol timing for a single multicarrier symbol can be obtained using forward-only processing. The proposed receiver computes continuous-time symbol likelihoods along with a timing metric and its time derivative. The timing metric shows a sharp concentration that is used to produce a trigger to sample the matched filter outputs. The paper proposes baseband processing using message passing algorithms derived from factor graphs. Suitable system parameters for which near-optimum synchronization is obtained are identified using computer simulations.

Discovering application dependency on data and storage is a key prerequisite for many storage optimization tasks such as data assignment to storage tiers, storage consolidation, virtualization, and handling unused data. However, in the real world these dependencies are rarely known, and discovering them is a challenge because of virtualization at various levels and the need for discovery methods to be non-intrusive. As a result, many optimization tasks are performed, if at all, without the full knowledge of application-to-storage dependencies.This paper presents a non-intrusive application-to-storage discovery method, and while it is built on our prior work, the storage discovery described here is entirely new. We used this discovery method in two production enterprise environments, consisting of about 323 servers, and we show how the discovered data enables three optimization tasks. First, we relate application criticality with storage tiers. Second, we find unused storage devices and we show how this information together with storage consolidation can be used to achieve power savings of up to two orders of magnitude. Third, we identify opportunities for database storage optimization.

This paper addresses the important problem of finding relevant information in the context of a business process. It presents an information access solution called EIL (enterprise information leverage) which combines information extraction and semantic search to support information needs of professionals selling IT services. EIL leverages structured and unstructured data using novel architecture and special purpose algorithms. Our approach is to organize information around business activities (e.g. a sale), and the system supports semantic concept based information retrieval by utilizing both database query and document search where the relevant business activities act as a contextual constraint. We experimentally show that this approach is promising for reducing noise in search results. EIL is currently under pilot deployment in one of the IBM services sales units.

An enterprise typically operates multiple data center sites, each handling workloads according to an enterprise-level strategy. Sharing resources across multiple sites (or enterprises) brings up several important problems. Each site may have its own policies that govern its interactions with other remote sites. Different policies impact the system performance in different ways. The site administrators and system designers need to understand the effects of a given set of policies on different workloads. In this paper, we describe an analysis methodology that determines the impact of policies on the workloads, and we present results and validation for a prototypical multi-site resource sharing system. Our analytical tool is capable of evaluating complex policies on a large scale system and permits independent policies for each site, so that policy makers can quickly evaluate several alternatives and their effects on the workloads before deploying them.

In large networked systems, relationships between applications and the data that they use through multiple tiers of middleware systems are often invisible. While the benefits of knowing such relationships are clear from a systems management perspective, discovery of such relationships is complicated by the widespread adoption of virtualization technologies and the tendency to view each middleware tier as an independent "domain" from a systems management perspective. In this paper we present a methodology and a system for automatic discovery of end-to-end application-data relationships. The key to the methodology is the modeling of data locations from which applications use data and of how middleware systems make data available to software layers above them.

We present a policy-based architecture STEPS for lifecycle management (LCM) in a mass scale distributed file system. The STEPS architecture is designed in the context of IBM's SAN file system (SFS) and leverages the parallelism and scalability offered by SFS, while providing a centralized point of control for policy-based management. The architecture uses novel concepts like policy cache and rate-controlled migration for efficient and non-intrusive execution of the LCM functions, while ensuring that the architecture scales with very large number of files. The architecture has been implemented and used for lifecycle management in a distributed deployment of SFS with heterogeneous data. We conduct experiments on the implementation to study the performance of the architecture. We observed that STEPS is highly scalable with increase in the number as well as the size of the file objects hosted by SFS. The performance study also demonstrated that most of the efficiency of policy execution is derived from policy cache. Further, a rate-control mechanism is necessary to ensure that users are isolated from LCM operations.

This article presents the deign and implementation of the recovery scheme in Calypso. Calypso is a cluster-optimized, distributed file system for UNIX clusters. As in Sprite and AFS, Calypso servers are stateful and scale well to a large number of clients. The recovery scheme in Calypso is nondisruptive, meaning that open files remain open, client modified data are saved, and in-flight operations are properly handled across server recover. The scheme uses distributed state amount the clients to reconstruct the server state on a backup node if disks are multiported or on the rebooted server node. It guarantees data consistency during recovery and provides congestion control. Measurements show that the state reconstruction can be quite fast: for example, in a 32-node cluster, when an average node contains state for about 420 files, the reconstruction time is about 3.3 seconds. However, the time to update a file system after a failure can be a major factor in the overall recovery time, even when using journaling techniques.

The World-Wide Web is the 'killer application' of the mid-1990's, and as a result it serves as the market place for various services. This position document discusses the problem of creating complex Web services from simpler ones, under client control. For example, one might want an annotated forecast of a company's stock price from three simple services, viz., stock price data, forecasting and annotation. We propose meta-scripts as a solution. A client accesses a meta-script, which contains procedures for accessing and composing simpler services. Meta-identifiers are used in the meta-script instead of static links. Developments in type-consistency mechanisms and various enabling services of the Web help in realization of the meta-script based approach. This model of service composition separates control from data manipulation, and gives this control to a client. As clients are empowered with the selection and coordination of service providers, this can lead to a free market for services.

This paper describes design and preliminary measurements of a file server recovery scheme that uses naturally replicated state among clients. This scheme, implemented in the Calypso file system, is truly transparent to the user and avoids the overhead of explicit replication. A three-phase protocol reconstructs the server state either on a backup node (if disks are multi-ported) or on the rebooted server node. Measurements show that the recovery time is about 21 seconds for a busy 10-node cluster. However, the time to rebuild the distributed state is only about 1.5 seconds, and most of the recovery time is spent in replaying the write-ahead log of the underlying file system. Fortunately, the log redo time is bounded by the log size.

Presents dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30% better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50% improvements relative to their nonpredictive counterpart.>

We propose a new frequency-based replacement algorithm for managing caches used for disk blocks by a file system, database management system, or disk control unit, which we refer to here as data caches. Previously, LRU replacement has usually been used for such caches. We describe a replacement algorithm based on the concept of maintaining reference counts in which locality has been “factored out”. In this algorithm replacement choices are made using a combination of reference frequency and block age. Simulation results based on traces of file system and I/O activity from actual systems show that this algorithm can offer up to 34% performance improvement over LRU replacement, where the improvement is expressed as the fraction of the performance gain achieved between LRU replacement and the theoretically optimal policy in which the reference string must be known in advance. Furthermore, the implementation complexity and efficiency of this algorithm is comparable to one using LRU replacement.

A statistical approach is developed for predicting the CPU time, the file I/O, and the memory requirements of a program at the beginning of its life, given the identity of the program. Initially, statistical clustering is used to identify high-density regions of process resource usage. The identified regions form the states for building a state-transition model to characterize the resource usage of each program in its past executions. The prediction scheme uses the knowledge of the program's resource usage in its last execution together with its state-transition model to predict the resource usage in its next execution. The prediction scheme is shown to work using process resource-usage data collected from a VAX 11/780 running 4.3 BSD Unix. The results show that the predicted values correlate strongly with the actual; the coefficient of correlation between the predicted and actual values for CPU time is 0.84. The errors in prediction are mostly small and are heavily skewed toward small values.>