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Data Management Issues and Technologies for Location-Aware Computing

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林业院校中“数据科学导论”的课程改革探索

林业院校中“数据科学导论”的课程改革探索

计算机教学与教育信息化本栏目责任编辑：王力林业院校中“数据科学导论”的课程改革探索熊飞,曹涌,孙永科（西南林业大学大数据与智能工程学院，云南昆明650224）摘要：数据科学导论是数据科学与大数据专业中很重要的导论性课程，课程中涉及了统计学、计算机、机器学习和深度学习的大量前沿内容，具有理论复杂、知识点繁多的特点。

理工科基础较为薄弱的林业院校学生掌握难度较大。

本文提出了数据分析基础、机器学习与深度学习和数据管理与产品开发的三大模块构成的课程体系以及相应的教学模式，侧重于培养学生以数据为中心的思维模式，形成了符合林业院校特色的导论课程。

关键词:数据科学导论;课程改革;导论课程;林业院校;思维模式中图分类号：TP391文献标识码：A文章编号：1009-3044(2021)15-0147-03开放科学（资源服务）标识码（OSID ）：Exploration on Course Reform of Introduction to Data Science in Forestry Universities XIONG Fei,CAO Yong,SUN Yong-ke(College of Big Data and Intelligent Engineering,Southwest Forestry University,Kunming 650224,China)Abstract:Introduction to Data Science is an important introductory course for Data Science and Big Data Technology,which covers a wide range of cutting-edge content in statistics,computers,machine learning,and deep learning.Therefore learning of this course is a challenging work for students that whitweak foundations in science and engineering in forestry universities.A teaching model focus on cultivating a data-centric mindset is introduced in this paper,which includes three parts:data analysis,Machine learning and deep learning,data management and product development.The redesign of Introduction to Data Science makes it con⁃form to the characteristics of forestry university.Key words:introduction to data science;course reform;introductory course;forestry universities;1引言2015年由国务院印发了《国务院关于印发促进大数据发展行动纲要的通知》标志着国家把大数据上升到了国家战略的层面，随后在2016年教育部在《教育部高等教育司关于2016年度普通高等学校本科专业设置工作有关问题的说明》中增加了数据科学与大数据技术专业（专业代码：08910T ）来促进数据科学专业人才的培养。

资料科学基础英文版课件

资料科学基础英文版课件

Semi supervised learning is a type of machine learning that combines both labeled and unlabeled data for training
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Spread: Standard deviation, variation
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Shape: Skewness, kurtosis
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Relationships: Correlation, regression
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45%
50%
75%
85%
95%
Purpose: Draw conclusions about population from sample data
Overview of Data Collection and Sampling: In data science, data collection and sampling are important steps in obtaining data for analysis and modeling.
Data Science also helps in identifying risks, reducing uncertainties, and making better predictions about future trends and outcomes
The field of Data Science has evolved over the years, starting from the early days of data management and analysis to the current era of big data, machine learning, and artificial intelligence

科技与大数据的关系英语作文

科技与大数据的关系英语作文

The Intersection of Technology and Big Data In today's rapidly evolving technological landscape, the relationship between technology and big data has become increasingly intricate and profound. The two entities are not just interconnected but are, in fact, symbiotic, with each enhancing the capabilities and potential of the other. Big data, by definition, refers to the vast quantities of structured and unstructured information generated by various sources, including online transactions, social media interactions, and sensor readings from IoT devices. This data, often considered useless or irrelevant in isolation, holds immense value when analyzed andinterpreted correctly. Technology, on the other hand, provides the tools and platforms necessary to collect, store, process, and analyze this data efficiently.The advent of cutting-edge technologies like cloud computing, artificial intelligence, and machine learning has revolutionized the way we handle and leverage big data. Cloud computing, for instance, enables seamless storage and retrieval of vast amounts of data, making it accessible anytime, anywhere. AI and machine learning algorithms, onthe other hand, can sift through terabytes of data to identify patterns, trends, and insights that would be impossible to detect manually.The combination of technology and big data has opened up new avenues for innovation and growth across various industries. In healthcare, big data analysis can help doctors make more informed diagnoses and treatment decisions, while in retail, it can be used to predict consumer behavior and personalize marketing strategies. In the field of finance, big data analytics can assist in fraud detection and risk management. Even in areas like agriculture and transportation, the potential applications of big data are vast and diverse.Moreover, the integration of big data and technology is not just about extracting insights from past data; it's also about predicting future trends and outcomes.Predictive analytics, a subset of big data analytics, leverages historical data and algorithms to forecast future events, enabling businesses to make more informed decisions and strategic plans.However, the relationship between technology and big data is not without its challenges. Issues like data privacy, security, and ethical considerations are becoming increasingly pertinent as the amount of data generated and processed continues to grow. It's crucial to ensure that the benefits of big data analytics are balanced with the need to protect individuals' privacy and ensure data security.In conclusion, the intersection of technology and big data represents a transformative force that is reshaping our world. As we continue to develop more advanced technologies and generate more data, the potential for innovation and growth in this space is limitless. However, it's essential to approach this relationship with a balance of optimism and caution, ensuring that we harness the power of big data while also safeguarding against its potential pitfalls.**科技与大数据的交汇点**在当今快速发展的技术领域中，科技与大数据之间的关系变得日益复杂而深刻。

管理科学-数据模型与决策简介

管理科学-数据模型与决策简介
Data, Model and Decisions 数据、模型与决策
Introduction to Management Science 管理科学简介
What is Data, Model and Decisions 数据模型与决策是什么
管理者 信息提供 模型
结论
决策
执行
反馈
结果
管理者在组织内制定决策，数据、模型与决策的目的 是在科学、符合逻辑和合理的基础上制定决策。内容 主要是管理科学和统计学。
Fixed Cost = $50,000 (if Q > 0) Variable Cost = $400 Q Total Cost =
0, if Q = 0 $50,000 + $400 Q, if Q > 0
Profit:
Profit = Total revenue – Total cost
为管理科学实践者颁发的最负盛名的奖项是 弗兰茨·厄德曼(Franz Edelman) 奖。这些奖项授予全世界年度 管理科学的最佳应用。评奖活动由国际运筹学会、管理科学协会 、管理科学实践学会联合发起。
Theory of Quantitative Analysis 管理定量分析理论
解决方法
线性规划 整数规划 非线性规划 多目标规划 预测 网络分析 决策分析 库存模型 模拟
实际问题
Many real world examples 许多实际问题举例
▪ Breakeven point Analysis 盈亏平衡分析 ▪ Optimum loan plan of the bank银行的最优贷 款计划 ▪Portfolio selection 投资方案的选择 ▪ Optimal payback plan for the investment 最优 还款计划

高二英语科技词汇单选题40题(带答案)

高二英语科技词汇单选题40题(带答案)

高二英语科技词汇单选题40题(带答案)1.The new smartphone has a large _____.A.screenB.keyboardC.mouseD.printer答案：A。

“screen”是屏幕，新智能手机有一个大屏幕，符合常理。

“keyboard”是键盘，“mouse”是鼠标，“printer”是打印机，都与智能手机不匹配。

2.We can use a _____ to take pictures.puterB.cameraC.televisionD.radio答案：B。

“camera”是相机，可以用来拍照。

“computer”是电脑，“television”是电视，“radio”是收音机，都不能用来拍照。

3.The _____ can play music and videos.ptopB.speakerC.projectorD.scanner答案：A。

“laptop”是笔记本电脑，可以播放音乐和视频。

“speaker”是扬声器，“projector”是投影仪，“scanner”是扫描仪，都不能播放音乐和视频。

4.My father bought a new _____.A.tabletB.bookC.penD.pencil答案：A。

“tablet”是平板电脑。

“book”是书，“pen”是钢笔，“pencil”是铅笔，只有平板电脑是科技设备。

5.The _____ is very useful for online meetings.A.headphoneB.microphoneC.speakerD.camera答案：D。

“camera”摄像头在在线会议中很有用。

“headphone”是耳机，“microphone”是麦克风，“speaker”是扬声器，都不如摄像头在在线会议中的作用直接。

6.We can store a lot of data in a _____.A.flash driveB.penC.pencilD.book答案：A。

大数据管理与应用和统计学

大数据管理与应用和统计学

大数据管理与应用（Big Data Management and Applications）和统计学（Statistics）是两个相关且相互补充的领域。

它们在处理和分析大规模数据集方面发挥着重要作用，但侧重点和方法略有不同。

大数据管理与应用关注如何有效地存储、处理和管理大规模的数据集，以从中获取有价值的信息和洞察。

它涉及数据的收集、存储、清洗、整合和处理等方面。

该领域的技术和工具包括大数据存储系统（如分布式文件系统和数据库）、数据处理框架（如Hadoop和Spark）以及数据挖掘和机器学习算法等。

大数据管理与应用的目标是从大数据中发现模式、趋势和关联，为决策和业务提供支持。

统计学是一门研究收集、整理、分析和解释数据的学科。

统计学提供了一系列的方法和技术，用于描述和总结数据、进行推断和预测，并进行决策和推断的支持。

统计学涉及概率论、抽样方法、假设检验、回归分析等统计方法。

在大数据管理与应用中，统计学的方法可以用来分析和解释大规模数据集中的模式和关系，提供数据驱动的见解和预测。

大数据管理与应用侧重于数据的收集、存储和处理，以及从中提取有用的信息，而统计学则关注数据的分析、解释和推断。

它们共同构成了处理和应用大数据的综合方法，为数据驱动的决策和洞察提供支持。

浅谈数据治理、数据管理、数据资源与数据资产管理内涵及差异点

前言随着信息技术的不断涌现和普及，业务发展加快了数据膨胀的速度，行业内衍生了较多的新名词，如数据治理、数据管理、数据资源管理、数据资产管理等名词的定义很多，概念容易混淆，本文对这些名词术语及内涵进行系统的解析，便于读者对数据相关的概念有全面的认识。

一数据与数据管理（Data and Data Management）1.1数据数据（Data）是指所有能输入到计算机并被计算机程序处理的符号的介质的总称，是用于输入电子计算机进行处理，具有一定意义的数字、字母、符号和模拟量等的通称，是组成信息系统的最基本要素。

未来是智能时代，企业的决策机制将发生巨大变化，谁最先拥抱数据，谁就拥有更多智慧，谁就拥有更强竞争力，大数据技术将会推动人类无所不知、无所不晓、无所不能，助力无所不能的是无所不包的数据，未来十年，只有拥抱数据技术才是唯一选择。

1.2数据管理数据管理（Data Management）的概念是伴随上世纪八十年代数据随机存储技术和数据库技术的使用，计算机系统中的数据可以方便地存储和访问而提出的。

2015年，国际数据管理协会（DAMA，Data Management Association International）在DBMOK2.0知识领域将其扩展为11个管理职能，分别是数据架构、数据模型与设计、数据存储与操作、数据安全（Data Security）、数据集成与互操作性、文件和内容、参考数据和主数据（Master Data）、数据仓库（Data Warehouse）和商务智能（BI，Business Intelligence）、元数据（Metadata）、数据质量（Data二数据治理（Data Governance）2.1数据治理的定义数据治理（Data Governance）是一个正在不断发展的新兴学科，与众多新兴学科一样，目前数据治理存在多种定义，各大机构对数据治理的定义，如下表所2.2狭义的数据治理狭义的数据治理的驱动力最早源自两个方面：1）内部风险管理的需要，包括：财务做假、敏感数据涉密、数据质量差影响关键决策等。

数据管理 英文

数据管理英文Data ManagementData management refers to the process of organizing, storing, and retrieving data in a systematic and efficient manner. With the increasing volume and complexity of data in today's digital world, effective data management has become an essential aspect of business success.One key aspect of data management is data governance. Data governance involves the establishment of policies, procedures, and standards to ensure that data is managed and utilized in a consistent and reliable manner. This helps organizations ensure data quality, integrity, and security. Data governance also involves assigning roles and responsibilities to different individuals or teams to oversee data management processes.Data storage is another critical component of data management. There are various methods of data storage, including traditional relational databases, non-relational databases (such as NoSQL databases), and cloud storage solutions. The choice of storage method depends on factors such as the type and volume of data, as well as the organization's budget and infrastructure.Data retrieval is the process of accessing and extracting data from storage systems. This is typically done through querying databases or using data retrieval tools. Efficient data retrieval is crucial for timely decision-making and analysis.Data management also involves data cleansing or data cleaning.This is the process of identifying and correcting or removing errors, inconsistencies, or inaccuracies in data sets. Data cleansing is important for maintaining data integrity and accuracy.Another important aspect of data management is data security. Organizations must protect their data from unauthorized access, theft, or loss. This involves implementing security measures such as encryption, access controls, backups, and disaster recovery plans.Data management also extends to data integration and data migration. Data integration involves combining data from different sources or systems into a unified and consistent format. Data migration, on the other hand, refers to the process of transferring data from one system or storage medium to another.In recent years, data management has also been influenced by advancements in technology such as big data and artificial intelligence. Big data refers to large, complex data sets that are difficult to manage using traditional data processing methods. Organizations must adopt new technologies and techniques to effectively store, process, and analyze big data. Artificial intelligence can also help automate certain aspects of data management, such as data cleansing or data retrieval.In conclusion, data management is a crucial discipline for organizations in today's data-driven world. It involves various processes such as data governance, storage, retrieval, cleansing, security, integration, and migration. Effective data managementenables organizations to make informed decisions, enhance productivity, and gain a competitive edge.。

国内外科学数据管理办法研究进展

第1卷第3期2019年9月Vol.1，No.3Sept.2019农业大数据学报Journal of Agricultural Big Data国内外科学数据管理办法研究进展柏永青1，2，3杨雅萍1，3*孙九林1，3（1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京100101；2.中国科学院大学，北京100049；3.中国科学院地理科学与资源研究所国家地球系统科学数据中心，北京100101）摘要：科学数据是信息化时代重要的战略资源，高效管理和广泛流通是提升科学数据资源价值的关键途径。

信息技术的快速发展和科技计划的大量投入促使科学数据资源数量日益激增，对信息时代的科学数据管理提出了更大的挑战。

随着工业社会向信息社会的转变，国内外对科学数据管理的重视程度也在不断提高，各类数据管理机构和政府支撑部门通过建设数据集群、完善保障措施、优化发展理念、加大资助力度等方式，不断推动科学数据管理和共享走向成熟。

本文通过综合调研国内外科学数据管理的理念、政策、实践及成果，分析总结了国际上科学数据管理的先进经验，对标我国同类研究中存在的问题与挑战，提出了未来一段时间内中国科学数据管理发展的方向和建议：（1）建议持续规范各类科学数据资源管理，完善保障机制，提升标准化程度；（2）建议加强对数据资源的深层次挖掘，实现从数据到信息、知识、智慧和决策的升华；（3）建议加强数据科学技术人才培养，从政府层面落实数据科学家计划，为科学数据管理提供人才支撑；（4）建议拓宽国际合作渠道，加强合作的力度和广度，提升现有国家科学数据中心的国际影响力，为我国数据科学发展建设提供战略指导，为信息化时代的综合国力提升凝聚核心竞争力。

关键词：科学数据；数据管理办法；数据共享；数据平台；数据管理；科学数据管理；开放获取；数据评价中图分类号：G203文献标识码：A文章编号：2096-6369（2019）03-0005-16引用格式：柏永青,杨雅萍,孙九林.国内外科学数据管理办法研究进展[J].农业大数据学报,2019,01(03):5-20.Bai Y Q,Yang Y P,Sun J L.Advances in the Study of Domestic and Foreign Scientific Data Management Meth‐ods[J].Journal of Agricultural Big Data,2019,01(03):5-20.DOI：10.19788/j.issn.2096-6369.190301收稿日期：2019⁃07⁃15基金项目：中国科学院战略性先导科技专项（A类）——地球大数据科学工程（XDA19020304），中国工程院战略咨询研究项目——智慧农业发展战略研究（2019-ZD-5），国家地球系统科学数据中心（）（2005DKA32300），中国工程科技知识中心——地理资源与生态知识服务系统（）（CKCEST-2019-1-4）第一作者简介：柏永青，男，博士，研究方向：地球系统科学数据共享；Email：baiyq@通讯作者：杨雅萍，女，高级工程师，研究方向：地球系统科学数据共享；Email：yangyp@6农业大数据学报：专刊——科学数据管理第1卷第3期Advances in the Study of Domestic andForeign Scientific Data ManagementMethodsBai Yongqing1,2,3Yang Yaping1,3*Sun Jiulin1,3(1.State Key Laboratory of Resources and Environmental Information System,Institute of GeographicSciences and Natural Resources Research,Chinese Academy of Sciences,Beijing100101;2.University of Chinese Academy of Sciences,Beijing100049;3.National Earth System Science Data Center,Institute of Geographic Sciences and Natural Resources Re‐search,Chinese Academy of Sciences,Beijing100101)Abstract:Scientific data are important strategic resources in the information age.Efficient management and wide circulation can critically enhance the value of scientific data resources.Rapid informa‐tion technology developments and large investments in science and technology projects haveled to an explosion in the number of scientific data resources,which poses a greater challengeto scientific data management.The transformation from industrial society to information soci‐ety increases the importance of scientific data management,domestically and internationally.Many data management institutions and government departments promote robust scientific datamanagement and sharing through the construction of data clusters,improvement of securitymeasures,optimization of development concepts,and increased funding.This paper analyzesand summarizes the advanced experience of international scientific data management,based ona comprehensive survey of the concepts,policies,practices and achievements of scientific datamanagement at domestic and foreign institutions.It proposes future directions and suggestionsfor the development of scientific data management in China.Recommendations for the futureinclude the following:(1)Continuously standardize and improve the management of variousscientific data resources to ensure a mechanism to improve the standardization level.(2)Strengthen deep mining of data resources to realize the transformation from data to informa‐tion,knowledge,wisdom and decision-making.(3)Strengthen data science and technology tal‐ents training,implement data scientist programs from the government level,and provide talentsupport for scientific data management.(4)Broaden international cooperation channels,strengthen cooperation,promote the international influence of existing national science datacenters,provide strategic guidance for the development and construction of data science in Chi‐na,and build core competitiveness to enhance the comprehensive national strength in the infor‐mation age.Keywords:scientific data;data management methods;data sharing;data infrastructure;data management;scientific data management;open access;data evaluation第3期柏永青等：国内外科学数据管理办法研究进展1科学数据与科学数据管理科学数据是指人类在自然科学或工程技术科学领域通过实验、观测、探测、调查、挖掘等途径获取积累的用于科学研究活动的原始基础数据及衍生数据[1]，能够反映客观事物的本质、特征、变化规律[2-3]。

intercloud datamanagement

intercloud datamanagement Intercloud data management refers to the management and control of data in a distributed cloud environment. It involves the storage, movement, and processing of data across multiple cloud platforms and providers. In this article, we will explore the concept of intercloud data management and discuss its importance, challenges, and possible solutions.1. Introduction to Intercloud Data Management (200 words) Intercloud data management is a relatively new concept that has emerged with the growing popularity of cloud computing. As businesses and organizations increasingly adopt cloud solutions, the need for efficient and effective data management across multiple cloud platforms has become paramount. Intercloud data management enables seamless data integration, access, and analysis across different cloud environments.2. Importance of Intercloud Data Management (300 words) Effective intercloud data management is crucial for organizations that operate in a multi-cloud environment. It offers several benefits, including:a. Data accessibility: Intercloud data management ensures that data stored in different cloud platforms can be readily accessed and utilized. This facilitates collaboration, data sharing, and decision-making processes.b. Scalability and flexibility: With intercloud data management, organizations can easily scale their data storage and processing capabilities by leveraging multiple cloud providers. It allows them to adapt to changing business needs and avoid vendor lock-in.c. Data security and compliance: Intercloud data management enables organizations to enforce consistent security and compliance measures across different cloud platforms. It ensures that data is protected and meets regulatory requirements.3. Challenges in Intercloud Data Management (500 words)While intercloud data management offers numerous benefits, it also presents several challenges that organizations must address. Some of the key challenges include:a. Data integration: Integrating data from disparate cloud platforms is complex and requires a systematic approach. Organizations needto establish standardized data formats, schemas, and protocols to ensure seamless data integration across different clouds.b. Data movement and transfer: Moving large volumes of data between cloud providers can be time-consuming andresource-intensive. Organizations must consider factors such as bandwidth limitations, data transfer costs, and data sovereignty regulations when designing their intercloud data management strategy.c. Data governance and control: Organizations need to maintain control and governance over their data, even when it is distributed across multiple cloud environments. Ensuring data integrity, privacy, and compliance becomes a challenge when data is scattered across different clouds.d. Data security and privacy: Intercloud data management introduces additional security risks as data is processed and stored in multiple locations. Organizations must implement robust security measures, such as encryption, access controls, and auditing mechanisms, to protect data in transit and at rest.e. Vendor lock-in: Organizations using multiple cloud providers face the risk of vendor lock-in, where they become heavily dependent on a particular provider's services and interfaces. It can limit their flexibility and increase costs. Organizations need to assess interoperability and portability options when choosing cloud platforms.4. Solutions for Effective Intercloud Data Management (500 words) To address the challenges in intercloud data management, organizations can adopt various solutions and best practices:a. Data integration platforms: Implementing data integration platforms can facilitate seamless integration and movement of data across multiple cloud environments. These platforms enable data mapping, transformation, and synchronization between different clouds.b. Data virtualization: Data virtualization allows organizations to create a unified view of their distributed data assets, regardless of their physical location. It eliminates the need for physical data movement and enables real-time data access and analysis.c. Data governance and control frameworks: Organizations should establish robust data governance and control frameworks to maintain data integrity, privacy, and compliance across multiple cloud platforms. This includes implementing access controls, data monitoring, and auditing mechanisms.d. Encryption and security measures: Implementing encryption protocols and security measures at various levels, such as data in transit and at rest, can enhance the security of intercloud data management. It ensures that data remains protected, even when it traverses different cloud environments.e. Multi-cloud management solutions: Adopting multi-cloud management solutions allows organizations to centrally manage and control their distributed cloud resources. These solutions provide a unified interface for provisioning, monitoring, and managing data across multiple clouds.Conclusion (100 words)Intercloud data management plays a pivotal role in enabling seamless data integration, access, and analysis in a multi-cloudenvironment. While it presents various challenges, organizations can overcome them by adopting suitable solutions and best practices. With effective intercloud data management, organizations can unlock the full potential of their data and leverage the benefits offered by a distributed cloud infrastructure.。

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Proceedings of the Conference for the e-Democracy, pp. 203-211, Athens, Sept. 2003Data Management Issues and Technologiesfor Location-Aware ComputingDimitris Katsaros Yannis Manolopoulos Apostolos N. PapadopoulosDepartment of InformaticsAristotle University of Thessaloniki54006 Thessaloniki, GREECE{dkatsaro, yannis, apostol} @ delab.csd.auth.grAbstractModern applications pose high quality requirements with respect to the type of processing needed and the expected system performance. A modern research direction is location-aware computing, which aims at providing services to users taking into consideration the location of the user in space. In order for location-aware computing to be feasible, several data management issues must be addressed. Geographical data must be available in order to determine the position of user terminal devices. Efficient techniques must be available to compute efficiently the location of a terminal device. Moreover, algorithmic techniques that take into consideration the mobility of users must exist in order to predict a future location. Finally, effective and efficient data mining algorithms are needed in order to extract useful knowledge with respect to user demands and therefore increase the quality of services. In this paper we address the data management issues involved towards prov iding location-aware services.KeywordsData Management, Spatial Databases, Data Mining, Location-Aware Computing1. IntroductionMuch of the information that underpins the challenges facing society today — terrorist activities, global environmental ch ange and natural disasters — is geospatial in nature. An everyday example of key geospatial data is the location of a cell phone user who has dialed 100 in an emergency. In terrorist situations, the origins and destinations of phone calls, travel patterns of individuals, dispersal patterns of airborne chemicals, assessment of places at risk, and the allocation of resources are all geospatial data. As the volume of geospatial data increases byseveral orders of magnitude over the coming years, so will the potential for corresponding advances in knowledge of our world and in our ability to react to change.Thus, an interdisciplinary research and development initiative focusing on challenges in location-aware computing, databases, data mining and human-computer-interaction technologies has emerged. Its contribution lies in integrating diverse perspectives, especially on future cross-disciplinary research directions. As Figure 1 shows, the convergence of advances in these three areas, combined with a sharp increase in the quality and quantity of geospatial information, promises to transform our world. Geospatial data have become critically important in areas ranging from crisis management and public health to national secur ity and international commerce (Patterson 2003).Figure 1. The conve rgence of four independent technological advances can transform the world. Earlier we used the term location-aware computing, but what is exactly the meaning of this term? Location-aware computing is a special case of context-aware computing, which describes the special capability of an information infrastructure to recognize and react to a real-world context. Context, in this sense, comprises any number of factors, including user identity; current physical location; weather conditions; time of day, date, or season; and whether the user is asleep or awake, driving or walking. The most critical aspects of context are location and identity. Location-aware computing systems respond to a user’s location, either spontaneously (for instance, warning of a nearby hazard) or when activated by a user request (for example, is it going to rain in the next hour?).The paper is organized as follows. In Section 2 we cover the important issues that are related to data management. Several new trends are presented and explained along with their impact on location-aware computing. In Section 3 a detailed presentation is performed for the technologies that support location-aware computing. Finally, Section 3 concludes the paper.2. Data Management IssuesModern applications require the storage and management of diverse data types that are not so simple as number and characters (Silberchatz 1997, Manolopoulos 2000). For example, in order to be able to apply full text retrieval in a digital library application the database management system (DBMS) must provide advanced tools (Baeza -Yates 1999). As another example, consider an image database system storing medical images (e.g. MRI scans). Again the DBMS must provide techniques for efficient retrieval of similar images, given an image as input (image retrieval by content). In this section we focus on two data management research directions that are related to location -aware computing: geographical information systems and data mining .2.1 Geographical Data ManagementGeographical Information Systems (G.I.S.) are tools that enable the storage and retrieval of information about the spatial properties of objects (Rigaux 2001). Consider for example an application that requires the retrieval of objects based on the exact or approximate location of a reference object. A user may request for all objects that are contained in a specific geographic area (range query) or request for the 10 objects that are closer to a reference position (nearest -neighbor query). Usually, the data volume in such application is huge, and this poses another challenge for the DBMS. Figure 1 provides examples of range and nearest-neighbor queries.QD E (a) range query:find the areas that intersect rectangle Q (a) nearest-neighbor query:find the three cities closer to traveler Q Figure 1. (a) range query example, (b) nearest-neighbor query example.In order to answer such queries efficiently we require:• effective representation of the geospatial objects in the database• effective and efficient algorithms and access methods in order to provide the required answers fast.With respect to the first issue, advanced data types have been proposed to capture the semantics of the spatial properties (position, size, geometry). These data types must be supported by theDBMS as ordinary data types (numbers and characters). With respect to the second issue, advanced data structures have been investigated towards increased query processing efficiency. In some cases there is a need to predict the position of a set of objects in the near future. For example, consider the query “give the locations of the two nearest gas stations five minutes from now”. To answer such queries the concept of time must be captured in the DBMS. Combining spatial and temporal information in a DBMS is a n active research area, aiming at providing efficient representation and processing for spatio-temporal queries (queries that need spatial and temporal information to be answered).From the above discussion it is evident that efficient representation and retrieval of objects based on their location is of vital importance for high-quality location-aware services.2.2 Data MiningData Mining is another active research direction and aims at discovering knowledge from the underlying data (Han 2001, Dunham 2003). A major problem that arises in databases with huge data volumes, is that, data become unmanageable for humans. There is hidden information in these data that only specialized knowledge discovery algorithms can reveal. Figure 2 shows the role of data mining in the knowledge discovery process and the disciplines that has been affected by.(a) Data Mining as a part of the Knowledge Discovery Process (b) Data Mining has been affected by many disciplinesWith efficient data mining techniques the user can discover hidden patterns in the data and therefore draw conclusions about the data and their meaning. Some of the tools that data mining techniques offer are:•data clustering, grouping data into meaningful clusters,•state prediction, predicting future data states according to past and present states,•association rule mining, discovering relationships among the data.3. Technologies Supporting Location-Aware ComputingHaving, briefly, described the major issues of geographical information systems and data mining, we proceed with a more detailed discussion on location-aware computing. Location-aware computing is a new class of computing, which emerged due to the evolution of location sensing, wireless networking, and mobile computing. A location-aware computing system must be cognizant of its user’s state and surroundings, and must modify its behavior based on this information. A user’s context can be quite rich, consisting of attributes such as physical location, physiological state (e.g., body temperature and heart rate), emotional state (e.g., angry, distraught, or calm), personal history, daily behavioral patterns, and so on. If a human assistant were given such context, s/he would make decisions in a proactive fashion, anticipating user needs. In making these decisions, the assistant would typically not disturb the user at i nopportune moments except in an emergency.3.1Location sensing3.1.1The Global Positioning SystemThe Global Positioning System (GPS) is the most widely known location-sensing system today (GPSWorld). Using time-of-flight information derived from radio signals broadcast by a constellation of satellites (see Figure 2) in earth orbit, GPS enables a relatively cheap receiver (on the order of 100€ today) to deduce its latitude, longitude, and altitude to an accuracy of a few meters. Although certainly important, GPS is not a universally applicable location sensing mechanism, for several reasons. It does not work indoors, particularly in steel-framed buildings, and its resolution of a few meters is not adequate for many applications. GPS uses an absolute coordinate system, whereas some applications (for example, guidance systems for robotic equipment) require coordinates relative to specific objects. In addition, the specialized components needed for GPS impose weight, cost, and energy consumption requirements that are problematic for mobile hardware. Consequently, several other location sensing mechanisms have been developed, which vary significantly in their capabilities and infrastructure requirements. System costs vary as well, reflecting different trade-offs among device portability, device expense, and infrastructure needs.Figure 2. The GPS operational constellation.3.1.2.Wireless Indoor Geolocation systemsThe main elements of such a system (see Figure 3) are a number of location sensing devices that measure metrics related to the relative position of a mobile terminal (MT) with respect to aknown reference point (RP), a positioning algorithm that processes metrics reported by location sensing elements to estimate the location coordinates of MT, and a display system that illustrates the location of the MT to users. The location metrics may indicate the approximate arrival direction of the signal or the approximate distance between the MT and RP. The angle of arrival (AOA) is the common metric used in direction-based systems. The received signal strength (RSS), carrier signal phase of arrival (POA), and time of arrival (TOA) of the received signal are the metrics u sed for estimation of distance. As the measurements of metrics become less reliable,Figure 3. Functional diagram of an indoor geolocation system.3.2Wireless communicationsThe past decade has seen explosive growth in the deployment of wireless communication technologies. Although voice communication (cell phones) has been the primary driver, data communication technologies have also grown substantially. The IEEE 802.11 family of wireless LAN technologies is now widely embraced, with many vendors offering supporting hardware. Bluetooth is another standard, backed by a growing number of hardware and software vendors; although it offers no bandwidth advantage over 802.11, it was designed to be cheap to produce and frugal in its power demands. Infrared wireless communication is the lowest-cost wireless technology available today, primarily because it is the mass-market technology used in TV remote controls. Most laptops, many handheld computers, and some peripheral devices such as printers are manufactured today with built-in support for IrDA (the Infrared Data Association standard for wireless data transfer via infrared radiation). Infrared wireless communication must be by line of sight, with range limited to a few feet. High levels of ambient light also affect it adversely, such as prevail outdoors during daylight hours.3.3Mobile computingMobile computing is commonly associated with small-form-factor devi ces such as PDAs and tetherless (wireless) connectivity. Such devices provide access to information processing and communication capabilities, but do not necessarily have any awareness of the context in which they operate. The premise of mobile computing i s to provide connectivity in an “anyplace, anytime” fashion.Mobile computing emerged from the integration of cellular technology with the Web and it is not a special case of distributed computing, because it is characterized by four constraints:•Mobile elements are resource-poor relative to static elements. For a given cost and level of technology, considerations of weight, power, size and ergonomics will exact a penalty incomputational resources such as processor speed, memory size, and disk capacity. While mobile elements will improve in absolute ability, they will always be resource-poor relative to static elements.•Mobility is inherently hazardous. In addition to security concerns, portable computers are more vulnerable to loss or damage.•Mobile connectivity is highly variable in performance and reliability. Some buildings may offer reliable, high-bandwidth wireless connectivity while others may only offer low-bandwidth connectivity. Outdoors, a mobile client may have to rely on a low-bandwidth wireless network with gaps in coverage.•Mobile elements rely on a finite energy source. While battery technology will undoubtedly improve over time, the need to be sensitive to power consumption will not diminish. Concern for power consumption must span many leve ls of hardware and software to be fully effective.3.4 Data delivery alternatives in location-aware environmentsAs the world and its inhabitants are increasingly “wired,” individuals traveling through and between places have real-time access to an increasing variety of information, much of it geospatial in nature. Freeing users from desktop computers and physical network connections will bring geospatial information into a full range of real-world contexts, revolutionizing how people interact with the world around them. Imagine, for example, the ability to call up place-specific information about nearby medical services, to plan emergency evacuation routes during a crisis, or to coordinate the field collection of data on vector-borne diseases, using just a ha ndheld device.This new setting changed the way the information is delivered to the clients. Support for different styles of data delivery allows a distributed information system to he optimized for various server, client, network, data, and application p roperties. We can identify three main characteristics that can be used to compare data delivery mechanisms:•push versus pull.•periodic versus aperiodic.•unicast versus l-to-N.While there are numerous other dimensions that should he considered, such as fa ult-tolerance, error properties, network topology, etc. we have found that these three characteristics provide a good basis for discussing many popular approaches. Figure 4 shows these characteristics and how several common mechanisms relate to them.Figure 4. Data delivery options.3.4.1Client pull versus server pushCurrent database servers and object repositories manage data for clients that explicitly request data when they require it. When a request is received at a server, the server locates the information of interest and returns it to the client. This request-response style of operation is pull-based. In contrast, push-based data delivery involves sending information to a client population in advance of any specific request. With push-based delivery, the server initiates the transfer.3.4.2Aperiodic versus periodicBoth push and pull can be performed in either an aperiodic or periodic fashion. Aperiodic delivery i s event-driven - a data request (for pull) or transmission (for push) is triggered by an event such as a user action (for pull) or data update (for push). In contrast, periodic delivery is performed according to some pre-arranged schedule. This schedule may he fixed, or may he generated with some degree of randomness. An application that sends out stock prices on a regular basis is an example of periodic push, whereas one that sends out stock prices only when they change is an example of aperiodic push.3.4.3Unicast versus 1-to-NThe third characteristic of data delivery mechanisms is whether they are based on unicast or 1-to-N communication. With unicast communication, data items are sent from a data source (e.g., a single server) to one other machine, while 1-to-N communication allows multiple machines to receive the data sent by a data source. Two types of 1 -to-N data delivery can he distinguished: multicast and broadcast. With multicast, data is sent to a specific subset of clients who have indicated their interest in receiving the data. Since the recipients are known, given a two-way communications medium it is possible to make multicast reliable; that is, network protocols can be developed that guarantee the eventual delivery of the message to all clients that should receive it. In contrast, broadcasting sends information over a medium on which an unidentified and possibly unbounded set of clients can listen.Concluding RemarksLocation-aware computing is a special case of context-aware computing, which describes the special capability of an information infrastructure to recognize and react to a real-world context. Context, in this sense, comprises any number of factors, including user identity; current physical location; weather conditions; time of day, date, or season; and whether the user is asleep or awake, driving or walking.In this paper we briefly discussed how geospatial information processing and data mining tools can be combined with location-aware computing in order to provide high quality location-aware services. In our days, we are witnessing a tremendous research effort towards combining several disciplines together in order to provide more useful and robust systems with worldwide impact. ReferencesR. Baeza-Yates and B. Ribeiro-Neto, (1999) “Modern Information Retrieval”, Addison-Wesley. M.H. Dunham (2003), “Data Mining: Introductory and Advanced Topics”, Prentice Hall. GPSWorld: J. Han and M. Kamber, (2001) “Data Mining: Concepts and Techniques”, Morgan Kau fmann. Y. Manolopoulos, Y. Theodoridis and V.J. Tsotras, (2000) “Advanced Database Indexing”, Kluwer Academic Publishers.C.A. Patterson, R.R. Muntz, C.M. Pancake, (2003)“Challenges in Location-Aware Computing”, IEEE Pervasive Computing, Vol.2, No.2, pp.80-89.P. Rigaux, M. Scholl and A. Voisard, (2002) “Spatial Databases with Applications to GIS”, Morgan Kaufmann.A. Silberschatz, H.F. Korth and S. Sudarshan, (1997) “Database System Concepts’’, McGraw-Hill.。