Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose Dipole, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation.

Social media has become increasingly prevalent in the last few years, not only enabling people to connect with each other by social links, but also providing platforms for people to share information and interact over diverse topics. Rich user-generated information, for example, users’ relationships and daily posts, are often available in most social media service websites. Given such information, a challenging problem is to provide reasonable user and community recommendation for a target user, and consequently, help the target user engage in the daily discussions and activities with his/her friends or like-minded people. In this article, we propose a unified framework of recommending users and communities that utilizes the information in social media. Given a user’s profile or a set of keywords as input, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user or keywords. With the proposed framework, users can find other individuals or communities sharing similar interests, and then have more interaction with these users or within the communities. We present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topical interests of users, as well as the focuses of communities. Extensive experimental evaluation and case studies on a dataset collected from Twitter demonstrate the effectiveness of our proposed framework compared with other probabilistic-topic-model-based recommendation methods.

The tremendous growth and popularity of social media platforms like Twitter, Facebook, etc. provides business organizations an opportunity to monitor the feedback from its customers, identify their problems and take corrective measures. In this paper, we describe a system to automatically monitor and analyze customer feedback through various social media platforms like Facebook, Twitter, etc. and detect issues faced by the customers. Business organizations can use this system to engage with their customers and help alleviate the problems faced by them. The system uses statistical event detection techniques for identifying various customer issues. The system offers a batch version as well as real time version of event detection algorithm depending upon the client's requirements. We also describe a few case studies illustrating the utility of our proposed system for business organizations in identifying issues faced by their customers through social media channels.

Access to massive real-time user generated personal information from micro blogging services, such as Twitter and Facebook, has the potential to enable new location-based recommendation and advertising services. However, sparse user profile information and low adoption of per-message geo-coordinate information necessitates development of location detection techniques that exposes a user's location from message content. We propose and evaluate content-based machine learning techniques to a) identify tweets containing a user's location, and, b) categorize a user location into the author's present or future location. Such an approach is advantageous because it a) relies purely on message content, b) can be used to predict a user's future presence at a location, c) relates user locations to some context (activities, trip plans, etc.), and, d) can be used to profile users constantly evolving location. Our experimental evaluation shows that the proposed techniques can identify and categorize user locations from message content with high accuracy. We also extract the time entities associated with a user's future location to show when the user would be at that location. Finally we illustrate the location-based data analytics potential of these techniques on two real-world datasets.

In this paper, we propose a framework of recommending users and communities in social media. Given a user's profile, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user. In our framework, we present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topic interests of users, as well as the focuses of communities. Extensive evaluation on a data set obtained from Twitter has demonstrated the effectiveness of our proposed framework compared with other probabilistic topic model based recommendation methods.

Given a process specification, it is a complex task to dynamically select constituent services and compose them in an execution plan to satisfy users' non-functional preferences. Process scheduling approaches assume users can clearly specify their non-functional preferences and there are formulas (e.g., utility functions) to compute process level QoS from the QoS of constituent services and their connections. However, these assumptions are not always true. Users' preferences can be subjective, implicit, vague, mixed and different for various types of processes. Besides, not all the preferences for example easy-to-use can be computed using formulas. We proposed a machine learning based approach to evolutionarily learn user preferences according to their ratings on historical execution plans, recommend existing or generate new execution plans for business processes that adapt to user preferences.

To gain competitiveness and sustained growth in the 21st century, most businesses are on a mission to become more customer-centric. In order to succeed in this endeavor, it is crucial not only to synthesize and analyze the VOC (the VO ice of the C ustomer) data (i.e., the feedbacks or requirements raised by customers), but also to quickly turn these data into actionable knowledge. Although there are many technologies being developed in this complex problem space, most existing approaches in analyzing customer requests are ad hoc, time-consuming, error-prone, people-based processes which hardly scale well as the quantity of customer information explodes. This often results in the slow response to customer requests. In this article, in order to mine VOC to extract useful knowledge for the best product or service quality, we develop a hybrid framework that integrates domain knowledge with data-driven approaches to analyze the semi-structured customer requests. The framework consists of capturing functional features, discovering the overlap or correlation among the features, and identifying the evolving feature trend by using the knowledge transformation model. In addition, since understanding the relative importance of the individual customer request is very critical and has a direct impact on the effective prioritization in the development process, we develop a novel semantic enhanced link-based ranking (SELRank) algorithm for relatively rating/ranking both customer requests and products. The framework has been successfully applied on Xerox Office Group Feature Enhancement Requirements (XOG FER) datasets to analyze customer requests.

Given a document collection, it is often desirable to find the core subset of documents focusing on a specific topic. We propose a new algorithm for this task. Document clustering aims at par- titioning the document-term datasets into differ- ent groups by optimizing certain objective func- tions. However, they are not suitable for finding hotspots that are described by a small set of doc- uments with few tightly coupled terms. In this pa- per we propose a novel hotspot finding algorithm, DCC (Dense Concept Clustering) in document collections. DCC can extract distinct small top- ics with most representative documents and words simultaneously. The hotspots are dense bicliques in binary document-word matrices and they can be discovered sequentially one at a time using the generalized Motzkin-Straus formalism. The rep- resentative documents and words are tightly cor- related for concept descriptions. Experiments on real document datasets show the effectiveness of the proposed algorithm.

Workflow modeling is playing an increasingly important role in representing end-to-end business process for workflow analysis, integration, automation and optimization purposes. This paper introduces an intelligent tool that automatically generates workflow models based on a set of given user constraints and underlying service capability ontology for production printing domain through an ease-to-use wizard-like interface. Unlike any existing workflow modeling tool, this intelligent wizard does not require the users with technical expertise in process modeling. The wizard-like interface provided in the proposed tool enables business people to easily create workflow models that match the user constraints provided via a set of questionnaire. The resulting workflow model is represented from three view points: service view, product view and device view. This paper discusses the tool's architecture and key components, such as domain ontology on user constraints and service capability, and ontology-based inference engine that implements the automatic workflow generation algorithm.

For today's business organizations, workflow models play important roles in analyzing the productivity, evaluating the performances and costs, optimizing the business operations, and supporting evolving services and products. Workflow mining, the process of empirically extracting structured process descriptions from a set of real executions, thus has attracted a lot of attention recently. However, there are several challenges that have not been fully addressed in the previous research: (i) How can we mine process models with optional tasks? (ii) How can we efficiently use new available workflow log data to incrementally update pre-existing workflow models or to complete previous partial process models? (iii) How can we compare two different workflow models of similar organizations? In this paper, we present our research efforts to address the above challenges. We present a workflow mining algorithm that is able to mine process models with optional tasks and propose an incremental workflow mining algorithm based on intermediate relationships such as ordering and independence. The intermediate relationships can also be used to facilitate the comparison of two process models. We illustrate our algorithms on example data derived from real world applications.

This paper presents an experimental study on cache memory designs for vector computers. We use an execution-driven simulator to evaluate vector cache performance of a set of application programs from Perfect Club and SPEC92 benchmark suites. Our simulation results uncover a few important facts which were unknown before: First of all, the prime-mapped cache that we newly proposed shows great performance potential in vector processing environment. Because of its conflict-free property, the prime-mapped cache performs significantly better than conventional cache designs for all applications considered. Second, performance results on the benchmarks indicate that data locality in vector processing does exist, although the effects of line size, associativity, replacement algorithm, and prefetching scheme on cache performance are very different from what has been commonly believed. A medium size vector cache (e.g., 128 Kbytes) eliminates the necessity for a large number of interleaved memory banks in vector computers. Our experiments show that the vector computer that has a medium size prime-mapped cache with small cache line size and limited amount of prefetching provides significant speedup over conventional vector computers without cache. Performance results reported in this paper can also provide guidance to general-purpose computer designers to enhance cache performance for numerical applications.

This paper presents a technique for minimizing chip-area cost of implementing an on-chip cache memory of microprocessors. The main idea of the technique is Caching Address Tags, or CAT cache, for short. The CAT cache exploits locality property that exists among addresses of memory references. By keeping only a limited number of distinct tags of cached data, rather than having as many tags as cache lines, the CAT cache can reduce the cost of implementing tag memory by an order of magnitude without noticeable performance difference from ordinary caches. Therefore, CAT represents another level of caching for cache memories. Simulation experiments are carried out to evaluate performance of CAT cache as compared to existing caches. Performance results of SPEC92 programs show that the CAT cache, with only a few tag entries, performs as well as ordinary caches, while chip-area saving is significant. Such area saving will increase as the address space of a processor increases. By allocating the saved chip-area for larger cache capacity, or more powerful functional units, CAT is expected to have a great impact on overall system performance.

This paper presents a technique for minimizing chip-area cost of implementing an on-chip cache memory of microprocessors. The main idea of the technique Caching Address Tags, or CAT cache for short. The CAT cache exploits locality property that exists among addresses of memory references for the purpose of minimizing chip area-cost of address tags. By keeping only a limited number of distinct tags of cached data rather than having as many tags as cache lines, the CAT cache can reduce the cost of implementing tag memory by an order of magnitude without noticeable performance difference from ordinary caches. Therefore, CAT represents another level of caching for cache memories. Simulation experiments are carried out to evaluate performance of CAT cache as compared to existing caches. Performance results of SPEC92 programs show that the CAT cache with only a few tag entries performs as well as ordinary caches while chip-area saving is significant. Such area saving will increase as the address space of a processor increases. By allocating the saved chip area for larger cache capacity, or more powerful functional units, CAT is expected to have a great impact on overall system performance.