Design and Implementation of the Self-Management Travel System

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1 Design and Implementation of the Self-Management Travel System Dongbei University of Finance and Economics School of Tourism and Hotel Management,Dalian, China Abstract Through skeleton system, rapid prototyping methods, the expansion of the knowledge base and reasoning mechanisms, this paper uses an object-oriented language development environment to design a small expert system applying to self-management of travel. Based on the existing network platform and combining with the actual situation of the electronic travel management, the system realizes the paperless office in order to improve office efficiency and quality of tourism management. Due to the existing network platform, the system uses asp.net + IIS + SQL Server 2005 as the development environment. It also makes full use of Microsoft Enterprise Library the third party mature plug to manage database resources in the development process, designs an optimal algorithm for database query and achieves system optimization which reduces the time of database query. 1.Introduction Keywords: Expert System, Travel Advisory, Mobile Computing With the development of the tourism industry, the tourism industry has become one of the pillar industries of the national economy in China. Each year thousands of people go on a trip, and they bring local income is not ignored, which is a lot of urban development tourism s reasons. In the era of rapid development of information technology and the Internet, travel information technology will greatly affect the process of information of the society as a whole. Information is the main pillar of the tourism business management activities [1], and also the basis of enterprise decision-making. Tourism enterprise is the user and information management of the beneficiaries; the current along with the unceasing development of information technology, tourism appears a network, balance the development trend of the words, thus increasing the tourism operators and managers of the complexity of the control. So tourism information management model research, become the focus of scholars analyses problems. Tourism information management mode is tourism enterprise in order to realize the corresponding information management goal. One can use repeatedly, and ensure the cost of a standardized system structure. Currently our country tourism has the small scale. The defects of the information construction are not perfect enough. So our country tourism should be actively for system reform, strengthen information construction, improve the efficiency of management of the tourism industry, and enhance the competitiveness of the tourism industry. All tourism enterprises are both users and the beneficiaries of information management. Expert System (ES)[2], as an important branch of artificial intelligence (AI)[3], is a computer system simulating the way of human experts to solve problems by analysis, judgment and reasoning according to the internal expert level knowledge. Expert system is an intelligent computer program system contains a large amount of some industry experts experience knowledge, through the human experts knowledge processing corresponding problems. In fact, a contains a large amount of professional knowledge and experience programming system, through the artificial intelligence technology and computer technology, according to some industry in one or more experts to provide knowledge and experience, and then carries on the analysis and judgment, solve the corresponding complicated problem. In some fields, it can establish and solve problems [4] at the expert level in human. Even exceed in some fields. Expert system is the most active, effective, rapid development and matures research field of artificial intelligence [5][6]. Many impressive results of artificial intelligence and expert systems research have been obtained in China in the eighties, and later have gone through a lot of detours and experienced a lot of frustration [7]. Recently, with the rapid development of computer and network communication technology, especially the massive popularity of the Internet and mobile communication devices, artificial intelligence and expert systems research active again [8]. Expert system in the tourism industry has also become the focus of many scholars. Journal of Convergence Information Technology(JCIT) Volume8, Number6,Mar 2013 doi: /jcit.vol8.issue6.24

2 2. The overall design of the system The experts of this system are the senior scholars in the field of tourism. Experts, based on the analysis of the structural elements of attractions tourists given, give advice and attractions judgment to determine the attractions they want to go, as detection model of judging criteria[9]. Knowledge engineer experts are responsible for system integration and analogy of the answers to the questions in order to find out the contradiction case and conceptual differences. Then correctly express the expert knowledge in the knowledge database of the computers for reasoning application. The knowledge engineers in our system are software designers. The user is known as the one who uses expert system. Before plan to travel, travel enthusiasts seek expert evaluation and recommended by expert systems where experts as consultants[10]. Tour assistances, such as the staff of the travel agency, use the expert system through mobile devices where as assistant. The workload calculation mainly includes five parts: travel guide, tour guide assignment, scenic introduced, costs introduction and personnel management. The tour guide assignment and costs introduction are calculated by the experimental project. The related modules functions are shown as Fig.1. Work load calculation Personal management Cost introduction Scenic introduction Guide assignment Travel guide Add Delete Query Export Calculate Audit 3. The realization of key technology Figure 1. Related module chart The data center of the tourism industry can be divided into five sub-systems, data acquisition system, data storage systems, data security system, disaster recovery & business continuity systems, and data mining & decision support system. 3.1.Data acquisition system It is the basic function of the data center and the foundation of normal operation to obtain the data required by the data center from other applications. The system import data of other system in the travel industry into data center with the help of interface and tool software. (1) Data of other travel industry is imported into data center by interface software, such as import the management data of Opera hotel into the data center. (2) The non-electronic information is imported into data center by electronic recognition system, such as input information of the China Tourism Yearbook into data center.

3 3.2. Data storage system The data storage system stores all the data in data center, and achieves data format specification The data storage system is the core of the entire data center, and all data resources in data center will be stored in the storage system. Storage system, the underlying support platform of the other subsystems, provides standardized data for specific business. Specific construction method is as follows (1)Information resource management platform manages standard specification such as classification and coding of structured information, index system results, the outcome of the data element, unstructured information classification and departments acquisition publishing duties specification. It through two enterprise service bus deployment provides a unified enterprise service integration platform to solve diverse problems of industry data exchange and interface, and provides a unified platform for enterprise and external data exchange. (2)It builds a uniform knowledge management platform, to realize classification, cataloging, unified retrieval of data sources dispersed in a variety of business systems and other industry, and realizes information collection, publishing process to achieve enterprise knowledge management, self-improvement and self-development. (3)The data storage system uses dynamic storage pool technology. According to storage capacity future required, dynamic storage pool technology allocates virtual disk capacity for application servers with no need to consider the actual capacity. When the physical storage capacity reaches a pre-set warning line, the system will give alarm and add new disk device to the storage pool, which called automatic expansion. 3.3.Data security system Figure2. Data storage system It is used to protect the data center from viruses and hackers, in order to build an information security system that treats security policy as a core and combines management, technology and operation and maintenance together. On the purpose of ensure the safety of data center and reliable operation, we realize a comprehensive range of protection for the data center of the tourism industry. The system provides advanced threat prevention that protects endpoints from targeted attacks and unknown attacks. It includes instantly available active protection technology and management control functions. Proactive technologies automatically analyze application behaviors and network communications to detect and block suspicious activities, while management control functions can deny specific device and application activities which is considered high risk for enterprises, even block specific actions based on the user's location.

4 Figure3. Data system model 3.4.Security system for disaster recovery & business continuity The system is used to improve the overall availability of the data center and provide disaster recovery services. It creates a travel industry data disaster recovery center, and provides disaster recovery services of tourism enterprise information systems. The establishment of a centralized data analysis application platform and data released displayed system realize unified management of publishing and data display and bring existing analysis applications systems in information system of the industry. It belongs to the decision-making level of the management of the tourism industry, and enhances the scientific, timeliness, effectiveness of management. Traditional disaster recovery system is directly built on each application server, installs backup software, set up disaster recovery strategy separately on each application server and remote backup to a disaster recovery center directly. When the business systems has less demanding on the performance of the application server and the network has not much pressure, traditional disaster recovery backup system could meet the requirement. However, with the expansion of business systems and increase in concurrent access of the user terminal, the manage stress of application systems, performance pressure of application server and pressure on the network are also grown, at this time the traditional distributed disaster recovery system will seriously influence the performance of application server and network. Traditional system of the overall structure described in figure 4. Figure4. Traditional disaster recovery system of the overall structure The program uses the integrated disaster recovery system, the platform adds a layer of performance & security guarantee on the traditional disaster recovery system, that is to add a local data backup & disaster recovery system, which greatly improve the performance and safety of the overall system. The system adds a disaster recovery server in the same city disaster recovery site, then through backup software of data remote disaster recovery to synchronously replicate data in local backup

5 disaster recovery server to the city disaster recovery server. This process is conducted between local disaster recovery server and the city disaster recovery center server which does not affect the running of the business system. Through the establishment of the alternate application system of the city disaster recovery site, the system takes over the production center for business services when the local production center suffer from the disaster, to avoid catastrophe losses, ensure the operation of business system as much as possible, and achieve application-level disaster recovery. After the disaster of local production center, data of city disaster recovery center can be copied back via network, thereby restoring the business system of local production center. During the copy process the operations of disaster recovery center will not be interrupted. The system adds a disaster recovery server in the offsite disaster recovery site, then through backup software of data remote disaster recovery to asynchronously replicate data in local backup disaster recovery server to the offsite disaster recovery server. This process is conducted between local disaster recovery server and the offset disaster recovery center server which does not affect the running of the business system. When local production center needs data restoring, data of offset disaster recovery center can be copied back via network, thereby restoring the business system of local production center. In order to enhance the data security of offsite backup, offsite backup system build CDP (continuous data protection) data snapshot as the data backup of history version. CDP (continuous data protection) snapshot data can provides data analysis and verification services at any time, without interfering with the operation of the business system and affecting the original data. Off-site data backup adopts asynchronous mode, backup data maintain on local, and remote backup only when the network is idle, in order to reduce the pressure on network. Traditional disaster recovery system is directly built on each application server, installs backup software, set up disaster recovery strategy separately on each application server and remote backup to a disaster recovery center directly. 3.5.Data mining and decision support system The system achieves the extraction and reuse of specification data of the data center. Data Mining is a process to extract implicit but potentially useful information and knowledge people do not know from a large number of incomplete, noises, fuzzy and random data. With the rapid development of information technology, the amount of data accumulated by people grows rapidly. How to extract useful knowledge from massive data becomes a top priority. Responsive to the need, data mining come into being developed which is the key step of the Knowledge Discovery in Database. Data mining capabilities of the data center in the tourism industry is the summary, statistics and analysis of variety specified information for users. The system establishes a central database using index system as the core, according to the content in basic indicators that teased out by the information resource planning, extracts, transforms, and loads data from various business systems in the industry to the specification database of the data center Logical structure, data mining is shown in FIG. 5:

6 Figure 5. logic chart data mining 3.6. The optimization techniques of the database SGA (System Global Area) is the workspace of the database, combining Oracle processes to form an Oracle database instance which used to manage database data and answer users' requests. SGA has three components: the database cache, shared pool area and log buffer. The memory area is configured by the corresponding parameters in the initialization file initsid.ora, the performance efficiency of which also is affected by the various parameters in initsid.ora. Connect to the database as the DBA, SGA settings information is got by executing the following statement: SQL>select * from v$sga. 1) Optimization of the data buffer When the user reading data, the server process first removes data from the data file storage on the disk, then stores the data in the data buffer from which back to the user. The process shows that the size of the data buffer has a direct impact on the access speed of the database. If the data buffer is too small, it will result in frequent reading disk file when there are lots of users and slow database running, which would impact the use of the application. Whether the data buffer has been configured reasonably can be determined by hit rate. The data buffer hit rate can better reflect the database performance. The detail calculation is shown as follow: SQL>select (1 - (sum(decode(name, 'physical reads', value, 0)) / (sum(decode(name, 'db block gets', value, 0)) + sum(decode(name, 'consistent gets', value, 0))))) * 100 "Hit Ratio" from v$sysstat; Hit Ratio is equal to which represents the proportions of the number of times request a block of data and the number of times provided by an Oracle database buffer. If the Hit ratio is lower than 98%, the data buffer should be increased whose size is decided by parameter db_block_size. The initial size of Oracle is 2KB. Suppose that there is a database whose with a size of 40KB, it needs 20 times I/O for 2KB database to complete the read process while only 5 times for 8KB database. Therefore, the more the buffer blocks in high-speed buffer area, the more possible for Oracle to find the same data and the more rapid the query speed. 2)Optimization of the shared pool Shared pool includes the library cache and data dictionary cache. Hit rate is the indicators to measure the performance of the two buffers. Shared pool is managed by LRU algorithm to ensure code and data dictionary used frequently can be stored in the shared pool. The query of data dictionary Hit rate: SQL>select (1 - (sum (get misses) / sum (gets))) * 100 "Hit Ratio" from v$rowcache; Query result is The query of Library cache hit rate: SQL>select sum(pins) / (sum(pins) + sum(reloads)) * 100 "Hit Ratio" from v$libraycache; The Hit Ratio is If the library cache of shared pool and hit ratio of data dictionary are lower than 95%, the value of share size in initsid.ora should be increased.

7 4. Experimental analysis In order to validate this paper analyzes independent travel information management system effectiveness, through the simulation results verify the performance of the system in this paper height, simulation operation of the environment is: Windows operating system, PC P4 T g, 4 gram, Inte182865G graphics, MATLAB6.0. Through the experimental analysis this paper design of independent travel information management system management efficiency and error rate, and then analyzes the performance of the system in this paper. The results of detailed respectively with figure 6 and figure 7 description: Figure 6.this paper system management efficiency Figure 7. this paper system management error rate Comprehensive analysis chart 6 and figure 7, the paper presents the design of independent travel management system to keep the efficiency of between 94% and 97%, management error rate keep between 2% and 4.6%, reached higher level. Finally shows that the design of the independent tourism management system performance is higher, can quickly and accurately deal with related issues, has made the satisfactory effect, and it has high application value. 5.Conclusions The travel expert intelligent system is an inevitable choice for the development of the information technology in tourism industry, a must for realization of Information Resources Integration in tourism industry, fully shared, effective use, and tapping the value of information resources, and a necessary tools to improve the scientific & efficiency of the decision-making management, innovation management, and the quality of decision management. Therefore, the construction of the data center of the tourism industry is the requirements of the times, the needs of the industry reform & development

8 and the inevitable process in the development of the tourism industry construction. 6. Acknowledgment This research is funded by the planning project (12TABG013) of China National Tourism Administration. 7.Reference [1] Xia Li, Juan Chen, "Research on Knowledge Acquisition and Reasoning Mechanism of Pathologic Diagnosis Expert System", JCIT, Vol. 7, No. 20, pp. 550 ~ 556, 2012 [2] WANG Jiancong, "The study on The Expert System Used for E-commerce Reputation Evaluation", JDCTA, Vol. 6, No. 22, pp. 188 ~ 194, 2012 [3] ZHANG Jingzong, "Research of Surveying and Mapping Based on Adaptive Fuzzy Neural Network and Expert System", JCIT, Vol. 7, No. 23, pp. 115 ~ 122, 2012 [4] Sang-Woong Lee, Jeong-Seon Park, "Microscopic Image Recognition-based Fish Disease Diagnosis System", JCIT, Vol. 6, No. 10, pp. 355 ~ 364, 2011 [5] LIU Jinye, GU Lize, LUO Shoushan, "An Anonymous Authentication Scheme for Mobile Communication Based on Third-Party", IJACT, Vol. 4, No. 16, pp. 45 ~ 54, 2012 [6] Peng Lu, Dongdai Zhou, Xiao Cong, Shanshan Qin, Shaochun Zhong, "Design and Implementation of Computerized Adaptive Testing System for Multi-Terminals", JDCTA, Vol. 6, No. 18, pp. 401 ~ 410, 2012 [7] He Ke, Chen Xue, "A Bandwidth Allocation Algorithm for Video Distributions in Vehicular Networks", IJACT, Vol. 4, No. 3, pp. 26 ~ 33, 2012 [8] Steven K.C. Lo, "A Cooperative Multi-Agent Environment for Mobile Peer-to-Peer Security System", JCIT, Vol. 6, No. 9, pp. 285 ~ 295, 2011 [9] Chen Ling, Ming Chen, Wenjun Zhang, Feng Tian, "AR Cloudlets for Mobile Computing", JDCTA, Vol. 5, No. 12, pp. 162 ~ 169, 2011 [10] Xu Wu, "A Stable Group-based Trust Management Scheme for Mobile P2P Networks", JDCTA, Vol. 5, No. 2, pp. 116 ~ 125, 2011

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