Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

Scalable and Energy Efficient Software Architecture for Human Behavioral Measurements

Understanding human behavior is central to many professions including engineering, health and the social sciences, and has typically been measured through surveys, direct observation and interviews. However, these methods are known to have drawbacks, including bias, problems with recall accuracy, and low temporal fidelity. Modern mobile phones have a variety of sensors that can be used to find activity patterns and infer the underlying human behaviors, placing a heavy load on the phone's battery. Social science researchers hoping to leverage this new technology must carefully balance the fidelity of the data with the cost in phone performance. Crucially, many of the data collected are of limited utility because they are redundant or unnecessary for a particular study question. Previous researchers have attempted to address this problem by modifying the measurement schedule based on sensed context, but a complete solution remains elusive. In the approach described here, measurement is made contingent on sensed context and measurement objectives through extensions to a configuration language, allowing significant improvement to flexibility and reliability. Empirical studies indicate a significant improvement in energy efficiency with acceptable losses in data fidelity

Analysis and optimization of task granularity on the Java virtual machine

Task granularity, i.e., the amount of work performed by parallel tasks, is a key performance attribute of parallel applications. On the one hand, fine-grained tasks (i.e., small tasks carrying out few computations) may introduce considerable parallelization overheads. On the other hand, coarse-grained tasks (i.e., large tasks performing substantial computations) may not fully utilize the available CPU cores, leading to missed parallelization opportunities. We focus on task-parallel applications running in a single Java Virtual Machine on a shared- memory multicore. Despite their performance may considerably depend on the granularity of their tasks, this topic has received little attention in the literature. Our work fills this gap, analyzing and optimizing the task granularity of such applications. In this dissertation, we present a new methodology to accurately and efficiently collect the granularity of each executed task, implemented in a novel profiler. Our profiler collects carefully selected metrics from the whole system stack with low overhead. Our tool helps developers locate performance and scalability problems, and identifies classes and methods where optimizations related to task granularity are needed, guiding developers towards useful optimizations. Moreover, we introduce a novel technique to drastically reduce the overhead of task-granularity profiling, by reifying the class hierarchy of the target application within a separate instrumentation process. Our approach allows the instrumentation process to instrument only the classes representing tasks, inserting more efficient instrumentation code which decreases the overhead of task detection. Our technique significantly speeds up task-granularity profiling and so enables the collection of accurate metrics with low overhead.We use our novel techniques to analyze task granularity in the DaCapo, ScalaBench, and Spark Perf benchmark suites. We reveal inefficiencies related to fine-grained and coarse-grained tasks in several workloads. We demonstrate that the collected task-granularity profiles are actionable by optimizing task granularity in numerous benchmarks, performing optimizations in classes and methods indicated by our tool. Our optimizations result in significant speedups (up to a factor of 5.90x) in numerous workloads suffering from fine- and coarse-grained tasks in different environments. Our results highlight the importance of analyzing and optimizing task granularity on the Java Virtual Machine

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Self-aware software architecture style and patterns for cloud-based applications

Modern cloud-reliant software systems are faced with the problem of cloud service providers violating their Service Level Agreement (SLA) claims. Given the large pool of cloud providers and their instability, cloud applications are expected to cope with these dynamics autonomously. This thesis investigates an approach for designing self-adaptive cloud architectures using a systematic methodology that guides the architect while designing cloud applications. The approach termed $Self-aware$ $Architecture$ $Pattern$ promotes fine-grained representation of architectural concerns to aid design-time analysis of risks and trade-offs. To support the coordination and control of architectural components in decentralised self-aware cloud applications, we propose a $Reputation-aware$ $posted$ $offer$ $market$ $coordination$ $mechanism$. The mechanism builds on the classic posted offer market mechanism and extends it to track behaviour of unreliable cloud services. The self-aware cloud architecture and its reputation-aware coordination mechanism are quantitatively evaluated within the context of an Online Shopping application using synthetic and realistic workload datasets under various configurations (failure, scale, resilience levels etc.). Additionally, we qualitatively evaluated our self-aware approach against two classic self-adaptive architecture styles using independent experts' judgment, to unveil its strengths and weaknesses relative to these styles