Toward the Semantic Desktop: The seMouse Approach

Modern computers have enjoyed increasing storage capacity, but the mechanisms that harness this storage power haven't improved proportionally. Whether current desktops have scaled to handle the enormous number of files computers must handle compared to just a few years ago is doubtful at best. Scalability includes not only fault tolerance or performance stability of tools for users to harness this power. The lack of appropriate structures and tools for locating, navigating, relating, and sharing bulky file sets is preventing users from harnessing their PCs' full storage power. Powering desktops with metadata, leading to the semantic desktop, is a promising way to realize this potential. The seMouse approach realizes the promising vision of the semantic desktop. This approach provides seamless integration between file-centered tooling and semantically aware, resource-centered applications.     

Semantic Web search based on ontological conjunctive queries

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontology-based Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.     

个人数据空间管理中的任务挖掘策略

在个人数据空间管理过程中,用户需要处理大量异质数据如邮件、文档、图片等.随着用户数据在数量和种类上的增多,如何有效管理这些数据,为用户提供有效的存储及查询服务成为一个具有挑战性的问题.传统数据管理工具如文件系统、桌面搜索工具等并未给用户提供足够的管理能力.究其原因,个人数据空间是由数据、用户以及服务三要素组成.而传统数据管理工具却忽略了用户这一要素,因此仅能在存储路径或全文索引的基础上提供服务.实际上数据与用户之间具有密不可分的联系,个人数据空间中的数据正是来自于用户行为.而用户行为是由一个一个任务组成的.挖掘个人数据中的任务,可以建立起数据间基于用户行为的语义关系,进而可以为用户提供任务视角的数据管理服务以及基于任务的查询服务.正是基于这一思想,提出了基于用户行为挖掘用户任务的方法.通过分析用户行为,发现个人数据由用户行为产生的时序关系,然后根据该时序关系生成用户的任务.实验证明该方法是有效的.     

Comparing keyword search to semantic search: a case study in solving crossword puzzles using the Google™ API

Keyword‐based search engines such as Google? index Web pages for human consumption. Sophisticated as such engines have become, surveys indicate almost 25% of Web searchers are unable to find useful results in the first set of URLs returned (Technology Review, March 2004). The lack of machine‐interpretable information on the Web limits software agents from matching human searches to desirable results. Tim Berners‐Lee, inventor of the Web, has architected the Semantic Web in which machine‐interpretable information provides an automated means to traversing the Web. A necessary cornerstone application is the search engine capable of bringing the Semantic Web together into a searchable landscape. We implemented a Semantic Web Search Engine (SWSE) that performs semantic search, providing predictable and accurate results to queries. To compare keyword search to semantic search, we constructed the Google CruciVerbalist (GCV), which solves crossword puzzles by reformulating clues into Google queries processed via the Google API. Candidate answers are extracted from query results. Integrating GCV with SWSE, we quantitatively show how semantic search improves upon keyword search. Mimicking the human brain's ability to create and traverse relationships between facts, our techniques enable Web applications to ‘think’ using semantic reasoning, opening the door to intelligent search applications that utilize the Semantic Web. Copyright © 2007 John Wiley & Sons, Ltd.     

Interoperability of Learning Objects Copyright in the LUISA Semantic Learning Management System

Abstract

Semantic Web technology is able to provide the required computational semantics for interoperability of learning resources across different Learning Management Systems (LMS) and Learning Object Repositories (LOR). The EU research project LUISA (Learning Content Management System Using Innovative Semantic Web Services Architecture) addresses the development of a reference semantic architecture for the major challenges in the search, interchange, and delivery of learning objects in a service-oriented context. One of the key issues, highlighted in this paper, is Digital Rights Management (DRM) interoperability. A Semantic Web approach to copyright management has been followed, which places a Copyright Ontology as the key component for interoperability among existing DRM systems and other licensing schemes like Creative Commons. Moreover, Semantic Web tools like reasoners, rule engines and semantic queries facilitate the implementation of an interoperable copyright management component in the LUISA architecture.     

In the News

"AI and the Mobile Cloud," explores the growing use of cloud computing to deliver AI-empowered applications to mobile devices. Over time, mobile devices could become the principal means by which users interact with cloud-based, autonomously operating agents. "A Fish Called Filose" describes efforts of researchers at four European universities to design a fishlike marine robot that can navigate shallow waters or tricky currents. "Desktop Data Organization Grows Up" describes the emerging semantic desktop, a personal version of the Semantic Web for organizing an individual's thousands of files, e-mail messages, and other digital data in an graph of related items.     

Performing content-based retrieval of humans using gait biometrics

In order to analyse surveillance video, we need to efficiently explore large datasets containing videos of walking humans. Effective analysis of such data relies on retrieval of video data which has been enriched using semantic annotations. A manual annotation process is time-consuming and prone to error due to subject bias however, at surveillance-image resolution, the human walk (their gait) can be analysed automatically. We explore the content-based retrieval of videos containing walking subjects, using semantic queries. We evaluate current research in gait biometrics, unique in its effectiveness at recognising people at a distance. We introduce a set of semantic traits discernible by humans at a distance, outlining their psychological validity. Working under the premise that similarity of the chosen gait signature implies similarity of certain semantic traits we perform a set of semantic retrieval experiments using popular Latent Semantic Analysis techniques. We perform experiments on a dataset of 2000 videos of people walking in laboratory conditions and achieve promising retrieval results for features such as Sex (mAP  =  14% above random), Age (mAP  =  10% above random) and Ethnicity (mAP  =  9% above random).     

领域知识文档的语义检索方法研究

语义检索是解决信息检索中准确度、人性化要求的一个非常有潜力的方法。通过对知识文档进行主题词标注,然后建立从词元→主题词→知识文档的二级索引结构;对用户的检索,进行查询词到主题词的转化,计算语义相似度,按照语义相似度算法进行排序文档。目前基于知识文档的语义检索系统已经在某集团公司进行部署和应用,取得了前5项结果命中用户总查询90%的效果,说明这种方法是语义检索的一种有效途径。     

Latency-aware virtual desktops optimization in distributed clouds

Distributed clouds offer a choice of data center locations for providers to host their applications. In this paper, we consider distributed clouds that host virtual desktops which are then accessed by users through remote desktop protocols. Virtual desktops have different levels of latency-sensitivity, primarily determined by the actual applications running and affected by the end users’ locations. In the scenario of mobile users, even switching between 3G and WiFi networks affects the latency-sensitivity. We design VMShadow, a system to automatically optimize the location and performance of latency-sensitive VMs in the cloud. VMShadow performs black-box fingerprinting of a VM’s network traffic to infer the latency-sensitivity and employs both ILP and greedy heuristic based algorithms to move highly latency-sensitive VMs to cloud sites that are closer to their end users. VMShadow employs a WAN-based live migration and a new network connection migration protocol to ensure that the VM migration and subsequent changes to the VM’s network address are transparent to end-users. We implement a prototype of VMShadow in a nested hypervisor and demonstrate its effectiveness for optimizing the performance of VM-based desktops in the cloud. Our experiments on a private as well as the public EC2 cloud show that VMShadow is able to discriminate between latency-sensitive and insensitive desktop VMs and judiciously moves only those that will benefit the most from the migration. For desktop VMs with video activity, VMShadow improves VNC’s refresh rate by 90% by migrating virtual desktop to the closer location. Transcontinental remote desktop migrations only take about 4 min and our connection migration proxy imposes 13 μs overhead per packet.     

Mobile computer Web-application design in medicine: some research based guidelines

Designing Web-applications is considerably different for mobile computers (handhelds, Personal Digital Assistants) than for desktop computers. The screen size and system resources are more limited and end-users interact differently. Consequently, detecting handheld-browsers on the server side and delivering pages optimized for a small client form factor is inevitable. The authors discuss their experiences during the design and development of an application for medical research, which was designed for both mobile and personal desktop computers. The investigations presented in this paper highlight some ways in which Web content can be adapted to make it more accessible to mobile computing users. As a result, the authors summarize their experiences in design guidelines and provide an overview of those factors which have to be taken into consideration when designing software for mobile computers. “The old computing is about what computers can do, the new computing is about what people can do” (Leonardo’s laptop: human needs and the new computing technologies, MIT Press, 2002).     

Reducing Query Latencies in Web Search Using Fine-Grained Parallelism

Semantic Web search is a new application of recent advances in information retrieval (IR), natural language processing, artificial intelligence, and other fields. The Powerset group in Microsoft develops a semantic search engine that aims to answer queries not only by matching keywords, but by actually matching meaning in queries to meaning in Web documents. Compared to typical keyword search, semantic search can pose additional engineering challenges for the back-end and infrastructure designs. Of these, the main challenge addressed in this paper is how to lower query latencies to acceptable, interactive levels. Index-based semantic search requires more data processing, such as numerous synonyms, hypernyms, multiple linguistic readings, and other semantic information, both on queries and in the index. In addition, some of the algorithms can be super-linear, such as matching co-references across a document. Consequently, many semantic queries can run significantly slower than the same keyword query. Users, however, have grown to expect Web search engines to provide near-instantaneous results, and a slow search engine could be deemed unusable even if it provides highly relevant results. It is therefore imperative for any search engine to meet its users’ interactivity expectations, or risk losing them. Our approach to tackle this challenge is to exploit data parallelism in slow search queries to reduce their latency in multi-core systems. Although all search engines are designed to exploit parallelism, at the single-node level this usually translates to throughput-oriented task parallelism. This paper focuses on the engineering of two latency-oriented approaches (coarse- and fine-grained) and compares them to the task-parallel approach. We use Powerset’s deployed search engine to evaluate the various factors that affect parallel performance: workload, overhead, load balancing, and resource contention. We also discuss heuristics to selectively control the degree of parallelism and consequent overhead on a query-by-query level. Our experimental results show that using fine-grained parallelism with these dynamic heuristics can significantly reduce query latencies compared to fixed, coarse-granularity parallelization schemes. Although these results were obtained on, and optimized for, Powerset’s semantic search, they can be readily generalized to a wide class of inverted-index search engines.     

可移动的虚拟桌面:无线云终端

无线云终端的手段之一可以使手机用户获取运行在机房服务器的手机虚机的桌面。由于手机品牌繁多,这一手段促进了移动终端的中央管理。介绍此技术的方法,包括手机虚机的基础平台和管理软件。测试结果显示:大批量的用户可以经过3G无线传输使用Android和WinMobile的桌面。在未来,移动终端还有潜力同时获取虚拟桌面、视频,以及传输物联网应用的传感信息,并使用机房里小巧的手机虚机及物理服务器的能力。     

SWAN: A distributed knowledge infrastructure for Alzheimer disease research

SWAN – a Semantic Web Application in Neuromedicine – is a project to develop an effective, integrated scientific knowledge infrastructure for Alzheimer Disease (AD) researchers, enabled by Semantic Web technology and deployed on Alzforum (www.alzforum.org), a scientific web community for AD research. This infrastructure may later be deployed for research communities in other neuromedical disorders. SWAN incorporates the full biomedical research knowledge lifecycle in its ontological model, including support for personal data organization, hypothesis generation, experimentation, lab data organization, and digital pre-publication collaboration. Community, laboratory, and personal digital resources may all be organized and interconnected using SWAN's common semantic framework.     

Software agents and agency: A personal information management perspective

This paper considers the concept of agency and the applications of software agents within the field of Personal Information Management (PIM). PIM addresses the complex activities undertaken by individuals when organising their personal information. In the context of Personal Information Management, effective software agents may allow users to obtain information relevant to their tasks, and present it in a form that is directly targeted to the needs of the user.This paper concentrates on the notion of agency and its direct application to PIM tasks. A user-driven approach to the design of agent-based systems is presented. We argue that agent systems will only be successful if both usersand their tasks act as the bases for the design of such systems. An example task domain (searching the World-Wide Web) is introduced and a taxonomy of Web agents for the domain is discussed. Technical issues raised during the preliminary implementation of Web agents are also introduced.     

Multi-domain collaborative exploration mechanisms for query expansion in an agent-based filtering framework

Novice users often do not have enough domain knowledge to create good queries for searching information on-line. To help alleviate the situation, exploration techniques have been used to increase the diversity of the search results so that not only those explicitly asked will be returned, but also those potentially relevant ones will be returned too. Most existing approaches, such as collaborative filtering, do not allow the level of exploration to be controlled. Consequently, the search results can be very different from what is expected. We propose an exploration strategy that performs intelligent query processing by first searching usable old queries, and then utilising them to adapt the current query, with the hope that the adapted query will be more relevant to the user’s areas of interest. We applied the proposed strategy to the implementation of a personal information assistant (PIA) set up for user evaluation for 3 months. The experimental results showed that the proposed exploration method outperformed collaborative filtering, and mutation and crossover methods by around 25% in terms of the elimination of off-topic results.     

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

This paper presents the scalable on-line execution (SOLE) algorithm for continuous and on-line evaluation of concurrent continuous spatio-temporal queries over data streams. Incoming spatio-temporal data streams are processed in-memory against a set of outstanding continuous queries. The SOLE algorithm utilizes the scarce memory resource efficiently by keeping track of only the significant objects. In-memory stored objects are expired (i.e., dropped) from memory once they become insignificant. SOLE is a scalable algorithm where all the continuous outstanding queries share the same buffer pool. In addition, SOLE is presented as a spatio-temporal join between two input streams, a stream of spatio-temporal objects and a stream of spatio-temporal queries. To cope with intervals of high arrival rates of objects and/or queries, SOLE utilizes a load-shedding approach where some of the stored objects are dropped from memory. SOLE is implemented as a pipelined query operator that can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Performance experiments based on a real implementation of SOLE inside a prototype of a data stream management system show the scalability and efficiency of SOLE in highly dynamic environments. This work was supported in part by the National Science Foundation under Grants IIS-0093116, IIS-0209120, and 0010044-CCR.     

Dedicated workspaces: Faster resumption times and reduced cognitive load in sequential multitasking

Studies show that virtual desktops have become a widespread approach to window management within desktop environments. However, despite their success, there is no experimental evidence of their effect on multitasking. In this paper, we present an experimental study incorporating 16 participants in which a traditional Windows 7 environment is compared to one augmented by virtual desktops. Within the experimental condition, each virtual desktop acts as a dedicated workspace devoted to an independent goal-oriented task, as opposed to the control condition where only one single workspace is available to perform the same tasks. Results show that adopting virtual desktops as dedicated workspaces allows for faster task resumption (10 s faster on average) and reduced cognitive load during sequential multitasking. Within our experiment the majority of users already benefited from using dedicated workspaces after three switches to a previously suspended task, as the time lost on setting up workspaces was compensated for by faster subsequent task resumption. These results provide a strong argument for supporting goal-oriented dedicated workspaces within desktop environments.     

基于虚拟组织的桌面云安全访问与共享机制研究

采用云计算技术实现托管式的虚拟桌面一般被称为桌面云。近年来桌面云被认为是云计算最为成熟的应用之一,本文着重研究桌面云安全访问与共享机制。我们使用基于PKI的证书认证建立了虚拟组织,在其上重点研究了虚拟机的创建,远程桌面访问,共享等应用。证书认证等机制可以使得访问更加安全可靠。而通过虚拟组织的信任关系,多个用户可以共享同一个虚拟机。为了确保远程通道的安全,我们采用了OpenVPN来构建虚拟专用网络,对虚拟机的使用者进行认证并对通信进行加密保护。     

Supporting Exploratory Analysis with the Select & Slice Table

In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select & Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select & Slice table during exploratory analysis of multivariate data.