2013

信息技术新进展国际高峰论坛(IFAIT2013)

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中国 镇江

2013/10/16-17

English Page

近年来，信息技术取得了许多新进展。大数据为我们提供了许多新的机会，它使得数据处理平台大大增加了其商业价值，效率，与产出，同时也给信息技术提供了许多新的挑战。 面向智慧城市的信息技术为现代化城市的发展提供了新的机遇。语义技术为许多应用系统的数据整合提供了有效的方法。这一类的信息技术的新进展不胜枚举。

2013信息技术新进展国际高峰论坛（IFAIT2013）为国际上的一流科学家提供了一个讨论这个新技术发展的讲台。 这个国际高峰论坛是江苏科技大学八十年校庆活动的一部分。在时间上与在南京召开的2013国际网络信息系统工程学术大会（WISE2013）相衔接, 即作为WISE2013国际学术大会的后续活动之一。

2013信息技术新进展国际高峰论坛将于2013年10月16日-17日在江苏镇江举行。

高峰论坛基本信息

对所有被邀参加2013信息技术新进展国际高峰论坛（IFAIT2013）的嘉宾，江苏科技大学将承担其在中国境内城市往返的车费（例如，南京/上海/北京到镇江的往返车票），会议期间在当地的食宿费用（即，镇江酒店两个晚上的住宿费和两天的餐费），镇江市内旅游（例如，金山寺旅游）。

2013信息技术新进展国际高峰论坛（IFAIT2013）所有与会者和发言者仅限被邀嘉宾，会议不收任何注册费用。

地点信息

Map

Important Dates

Sept 1st, 2013

Notification of invited speakers

Oct 13th-15th, 2013

WISE2013

Oct 16th-17th, 2013

Forum

重要日期

2013/9/1

通知特邀发言者

2013/10/13-15

2013国际网络信息系统工程学术大会

2013/10/16-17

2013信息技术新进展国际高峰论坛

特邀报告

Title: How do different complexities in a network affect the optimal location of service centers?
Speaker: Professor Johan Håkansson
(Dalarna University, Sweden)
Abstract:The p-median model is used on complex optimization problems. P-median problem is often NP-hard to sole. Therefore some kind of heuristics is normally used. The more complex the problem becomes the longer it takes to find an optimal solution. Frequent questions are therefore how complex the configuration of the reality in the analytic model has to be to still produce satisfying solutions in a reasonable time. Here I will talk about this issue when the p-median model is used to locate p service centers by minimizing their distances to a geographically distributed demand (n). These optimal locations are sensitive to geographical context such as road network and demand points especially when they are asymmetrically distributed in the plane. Most studies focus on evaluating performances of the p-median model when p and n vary. The effects of variations in the road network have attained less attention, especially when it is applied in a real world context. The aim here is to talk about a study in which an analysis of how the optimal location solutions vary, using the p-median model, when the density in the road network is alternated. The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 service centers we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000.

Title: Big Data versus Semantics
Speaker: Professor William Wei Song
(Dalarna University, Sweden)
Abstract: Big data has been a hot topic for two years. It has been viewed as a big opportunity for a great variety of applications, from e-retailing systems (where e-CRM plays a major role as more and more customers become available), smart cities (or intelligent transport systems, where real-time surveillant and monitoring data collected from various sources enable to solve e.g. traffic congestion problems efficiently), to new energy systems. Many researchers and research projects at moment focus on the "big" data about the application domains in terms of the data collection, store, analysis, processing, use, and maintenance. Based on statistic methods as well as data mining and machine learning techniques, researchers are developing many approaches to handling of the problem of "understanding" big data. However, little efforts have been put in capturing the "semantics" of the big data. In this short talk, I try to explain why semantic modeling of big data is important, how to capture it, and how to represent it.

Title: The OpenPHACTS Discovery Platform: easy access to pharmacological data
Speaker: Dr. Ronald Siebes
(VU University Amsterdam, the Netherlands)
Abstract:Currently, pharmaceutical companies assemble their own in-house databases of pharmacological and physicochemical data. Each of these is built to fulfil a primary use-case, and often the language and metadata implemented is unique and little integration can be obtained with other databases. Repetition of the pharmacological data extraction, transformation and loading stages at each pharmaceutical company greatly hinders the drug discovery process. Open PHACTS seeks to improve data interoperability, and move away from this ‘information tomb’ approach. Open PHACTS integrates multiple publicly-available databases, creating links between the data present, allowing access to a vast data resource in a stable and rigorous infrastructure. The provenance of all data is easily assessed, and traceable back to the parent database, allowing the data quality to be evaluated. In addition to reducing barriers to drug discovery within the pharmaceutical industry, the Open PHACTS Discovery Platform allows scientists in academia and smaller companies unprecedented access to an integrated database of pharmacological information. The Open PHACTS Discovery Platform draws together and generates links between variably-sourced data so that industry, academia and small businesses can concentrate on drug discovery.

Title:Finding Semantics to detect the abnormality in Multiple Time Series
Speaker: Dr. Jing He
(Centre for Applied Informatics, Victoria University, Australia )
Abstract: Detecting/predicting anomalies from multiple correlated time series is valuable to those applications where a credible real-time event prediction system will minimise economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). This talk will introduce an effective and efficient methods for mining the anomalies of correlated multiple time series in online and real-time manner. It includes the detection/prediction of anomalies by analysing differences, changes, and trends in correlated multiple time series. The predicted anomalies often indicate the critical and actionable information in several application domains.

Title: WaaS – Wisdom as a Service
Speaker: Professor Ning Zhong
(Maebashi Institute of Technology, Japan)
Abstract:An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, “from things to data, information, knowledge, wisdom, services, humans, and then back to things.” The W2T cycle leads to a harmonious symbiosis among humans, computers and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet/Web of Things, big data and other research fields have provided just such an open system architecture with resource sharing/services. The next step is therefore to develop an open and interoperable content architecture with intelligence sharing/services for the organization and transformation in the “data, information, knowledge and wisdom (DIKW)” hierarchy. This talk presents Wisdom as a Service (WaaS) as a content architecture based on the “paying only for what you use” IT business trend. The WaaS infrastructure, WaaS economics, and the main challenges in WaaS research and applications are discussed. A case study is described to demonstrate the usefulness and significance of WaaS. Relying on the clouds (cloud computing), things (Internet of Things) and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications.

Title: Big Data meets Spatial Computing
Speaker: Professor Huajun Chen
(Zhejiang University, China)
Abstract: Spatial Computing is a set of ideas and technologies that will transform our lives by observing, understanding the space of the physical world, and navigating, communicating our relation to places in that world. The transformational potential of Spatial Computing is already evident. From Virtual Globes such as Google Earth to location based social networking services such as twitter or weibo, our society has benefited immensely from spatial technology. Spatial computing is data-intensvie and typical big data application. Typical spatial data sources include satellite remote sensing, mobile devices, sensor network etc. In this talk, we first survey the big data challenges brought up by spatial computing. We then introduce an ongoing big effort that aims to provide an integrated big data infrastructure for collecting, storing, managing and analyzing a variety of spatial data in China. Research over several novel big data solutions such as SpatialHBase, SpatialMapreduce, Spatial Reasoning that are tailored for spatial data processing are elaborated in details as well. The talk is summarized with several promising applications that will build upon the big spatial data infrastructure.

Title: 4Vs in Semantic Data Reasoning: Variety, Velocity, Volumn and Veracity.
Speaker: Dr. Jeff Pan
(University of Aberdeen, UK)
Abstract: (to be added)

Title: Big Data Related Research Issues and Progress
Speaker: Professor Chengqi Zhang
(Centre for QuantumComputation & Intelligent Systems (QCIS), University of Technology, Sydney)
Abstract:(to be added).

Title: Toward Scalable Reasoning over Annotated Semantic Data Using MapReduce
Speaker: Professor Guilin Qi
(Southeast University, China)
Abstract: The Resource Description Framework (RDF) is one of the major representation standards for the Semantic Web and is widely used in many applications of semantic techniques, such as Google and Baidu's knowledge graph. RDF Schema (RDFS) is used to describe vocabularies used in RDF descriptions. Recently, there is an increasing interest to express additional information on top of RDF data. Several extensions of RDF were proposed in order to deal with time, uncertainty, trust and provenance. All these specific domains can be modeled by a general framework called annotated RDF data. A recent work reported millions of triples with temporal information in a large knowledge base called Yago 2 and the number is still increasing. It is reasonable to expect more annotated RDF triples to be handled by semantic web applications. Therefore scalability will become an important issue for these applications. Existing work has shown that MapReduce is a scalable framework to perform large scale RDFS reasoning. This inspires us to solve the large scale annotated RDFS reasoning problem using MapReduce. We find that most of the optimizations for RDFS reasoning is also applicable for annotated RDFS reasoning. However, to reason with an arbitrary annotation domain, there are still some unique challenges that need to be handled. In this paper, we will discuss these challenges and solutions to tackle them. Our preliminary results show that our method is scalable for a specific domain: fuzzy domain.

Title:Social Media & Social Network Data Analytics
Speaker: Dr. Xue Li
(School of Information Technology and Electrical Engineering, University of Queensland Australia)
Abstract: Social media and networks are a popular place for people to express their opinions about consumer products, to organize or initiate social events, or to spread news. Some questions would be asked in order to understand the social media and social networks: how can we detect and predict the emerging sensitive events? How can we predict the propagation patterns of online micro-blogs? How can we understand people’s opinions about a current issue, a new product, or an important event? This talk is to report our recent research work on the social media and social networks data mining. A few application systems will be reported to answer the above questions.

Title:A Semantically-enabled System for Road Sign Management
Speaker: Dr. Qinhua Liu
(Jiangsu University of Science and Technology, China)
Abstract: The road sign is an important facility which manages the road traffic safety and eases the road traffic congestion. This paper proposes a Semantically-enabled System for Road Sign Management (SeRSM). The SeRSM system is built based on LarKC, which is a platform for scalable semantic data processing. In the SeRSM system, the users can select the corresponding operations through the interface integrated with a map service. These operations are sent to Jetty server for corresponding processing. They include sending some SPARQL query to invoke the corresponding workflow in the LarKC platform and to retrieve and reason the massive data stored in the data layer of LarKC and to return the result to the Jetty server. The paper made a full description of technical points such as the design objective, data sources, data integration, noisy data processing, detection of road consistency effectiveness. It also describes the system’s user interface and basic functions in the end. The SeRSM has great value and social significance for improving traffic efficiency and traffic safety through successful applications in Zhenjiang and Yiwu in China.

Title:A Minwise Hashing Method for Addressing Relationship Extraction from Text
Speaker: Dr. David Batista
(Technical Univeristy of Lisbon, Portugal)
Abstract:Relationship extraction concerns with the detection and classification of semantic relationships between entities mentioned in a collection of textual documents. This presentation shows a simple and on-line approach for addressing the automated extraction of semantic relations, based on the idea of nearest neighbor classification, and leveraging a minwise hashing method for measuring similarity between relationship instances. Experiments with three different datasets that are commonly used for benchmarking relationship extraction methods show promising results, both in terms of classification performance and scalability.

Title: Extracting entrie domains with DIADEM
Speaker: Dr. Christian Schallhart
(Computing Lab, Oxford University, UK)
Abstract:We are at the verge of fully automatically turning entire application domains into a single database. Given some knowledge on a domain, such as real estate, used cars, or flights, DIADEM explores an arbitrary site within that domain and identifies the relevant data on all explored pages to generate an efficient wrapper for this site. This entire process involves no per-site configuration.
To this end, DIADEM runs a comprehensive entity recognizer and a block classifier on each page to drive a form and result page analysis. If DIADEM recognizes a relevant form, it devises a strategy to fill this form to reach the data behind. Once a result page is found, it identifies its records and the attributes therein. It also follows a next link in case of paginated results. If sufficiently many pages have been explored, DIADEM induces a wrapper that reenacts the navigation and form filling and extracts the data, as marked by the result page analysis. The wrapper is finally run independently to extract the data into a database.
While the analysis takes a couple of minutes per site, probing only a few pages, the wrapper extracting the data is highly scalable, analyzing 10 pages a minute.

Title: Semantic Approach for Rational Use of Antibiotics: A Perspective from Clinical Research
Speaker: Professor Jinguang Gu
(Wuhan University of Science and Technology, China)
Abstract: Antibiotic abuse has potentially serious e ects on health. Rational use of antibiotics has become a basic principle in medical practice. In this paper we propose a semantic approach for rational use of antibiotics, by introducing the semantic technology into the monitoring on the use of antibiotic agents. In particular, we investigate the problem from the perspective of clinical research. The proposed approach has been implemented in a prototype system named SeSRUA, a Semantically-enabled System for Rational Use of Antibiotics. This semantic system with the support of data interoperability provides a basic infrastructure for the intelligent monitoring on the use of antibiotics.

(more invited speakers to be added)

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