In this paper, propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data.
TRAST model can be easily extended for computing relationships among many more tourists. However, the computation cost will also go up. To simplify the problem, in this paper, each time we only consider two tourists in a travel group as a tourist pair for mining their relationships. By this TRAST model, all the tourists’ travel preferences are represented by relationship distributions.
We
can use their relationship distributions as features to cluster them, so as to
put them into different travel groups. Thus, in this scenario, many clustering
methods can be adopted. Since choosing clustering algorithm is beyond the scope
of this paper, in the experiments, we refer to K-means one of the most popular
clustering algorithms.
INTRODUCTION:
AS an emerging trend, more and more travel companies provide online services. However, the rapid growth of online travel information imposes an increasing challenge for tourists who have to choose from a large number of available travel packages for satisfying their personalized needs. Moreover, to increase the profit, the travel companies have to understand the preferences from different tourists and serve more attractive packages. Therefore, the demand for intelligent travel services is expected to increase dramatically. Since recommender systems have been successfully applied to enhance the quality of service in a number of fields, it is natural choice to provide travel package recommendations. Actually, recommendations for tourists have been studied before and to the best of our knowledge, the first operative tourism recommender system was introduced by Delgado and DavidsonDespite of the increasing interests in this field, the problem of leveraging unique features to distinguish personalized travel package recommendations from traditional recommender systems remains pretty open.
Indeed, there are many technical and domain challenges inherent in designing and implementing an effective recommender system for personalized travel package recommendation. First, travel data are much fewer and sparser than traditional items, such as movies for recommendation, because the costs for a travel are much more expensive than for watching a movie. Second, every travel package consists of many landscapes (places of interest and attractions), and, thus, has intrinsic complex spatio-temporal relationships. For example, a travel package only includes the landscapes which are geographically colocated together. Also, different travel packages areusually developed for different travel seasons.
Therefore, the landscapes in a travel package usually have spatialtemporal autocorrelations. Third, traditional recommender systems usually rely on user explicit ratings. However, for travel data, the user ratings are usually not conveniently available. Finally, the traditional items for recommendation usually have a long period of stable value, while the values of travel packages can easily depreciate over time and a package usually only lasts for a certain period of time. The travel companies need to actively create new tour packages to replace the old ones based on the interests of the tourists. To address these challenges, in our preliminary work we proposed a cocktail approach on personalized travel package recommendation. Specifically, we first analyze the key characteristics of the existing travel packages. Along this line, travel time and travel destinations are divided into different seasons and areas. Then, wedevelop a tourist-area-season topic (TAST) model, which can represent travel packages and tourists by different topic distributions. In the TAST model, the extraction of topics is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. As a result, the TAST model can well represent the content of the travel packages and the interests of the tourists. Based on this TAST model, a cocktail approach is developed for personalized travel package recommendation by considering some additional factors including the seasonal behaviors of tourists, the prices of travel packages, and the cold start problem of new packages.
Finally, the experimental results on real-world travel data show that the TAST model can effectively capture the unique characteristics of travel data and the cocktail recommendation approach performs much better than traditional techniques. In this paper, we further study some related topic models of the TAST model, and explain the corresponding travel package recommendation strategies based on them. Also, we propose the tourist-relation-area-season topic (TRAST) model, which helps understand the reasons why tourists form a travel group. This goes beyond personalized package recommendations and is helpful for capturing the latent relationships among the tourists in each travel group.
In addition, we conduct systematic experiments on the realworld data. These experiments not only demonstrate that the TRAST model can be used as an assessment for travel group automatic formation but also provide more insights into the TAST model and the cocktail recommendation
approach. In summary, the contributions of the TAST model, the cocktail approaches, and the TRAST model for travel package recommendations are shown in Fig. 1, whereeach dashed rectangular box in the dashed circle identifies atravel group and the tourists in the same travel group are represented by the same icons.
LITRATURE SURVEY
COST-AWARE TRAVEL TOUR RECOMMENDATION
AUTHOR: Y. Ge et al.,
PUBLISH: Proc. 17th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’11), pp. 983-991, 2011.
Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation.
FLDA: MATRIX FACTORIZATION THROUGH LATENT DIRICHLET ALLOCATION
AUTHOR: D. Agarwal and B. Chen
PUBLISH: Proc. Third ACM Int’l Conf. Web Search and Data Mining (WSDM ’10), pp. 91-100, 2010.
We propose fLDA, a novel matrix factorization method to predict ratings in recommender system applications where a “bag-of-words” representation for item meta-data is natural. Such scenarios are commonplace in web applications like content recommendation, ad targeting and web search where items are articles, ads and web pages respectively. Because of data sparseness, regularization is key to good predictive accuracy. Our method works by regularizing both user and item factors simultaneously through user features and the bag of words associated with each item. Specifically, each word in an item is associated with a discrete latent factor often referred to as the topic of the word; item topics are obtained by averaging topics across all words in an item. Then, user rating on an item is modeled as user’s affinity to the item’s topics where user affinity to topics (user factors) and topic assignments to words in items (item factors) are learned jointly in a supervised fashion. To avoid overfitting, user and item factors are regularized through Gaussian linear regression and Latent Dirichlet Allocation (LDA) priors respectively. We show our model is accurate, interpretable and handles both cold-start and warm-start scenarios seamlessly through a single model. The efficacy of our method is illustrated on benchmark datasets and a new dataset from Yahoo! Buzz where fLDA provides superior predictive accuracy in cold-start scenarios and is comparable to state-of-the-art methods in warm-start scenarios. As a by-product, fLDA also identifies interesting topics that explains user-item interactions. Our method also generalizes a recently proposed technique called supervised LDA (sLDA) to collaborative filtering applications. While sLDA estimates item topic vectors in a supervised fashion for a single regression, fLDA incorporates multiple regressions (one for each user) in estimating the item factors.
MAP-BASED INTERACTION WITH A CONVERSATIONAL MOBILE RECOMMENDER SYSTEM,
AUTHOR: D. Agarwal and B. Chen
PUBLISH: Proc. Second Int’l Conf. Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM ’08), pp. 212-218, 2008.
Recommender systems are information search and decision support tools used when there is an overwhelming set of options to consider or when the user lacks the domain-specific knowledge necessary to take autonomous decisions. They provide users with personalized recommendations adapted to their needs and preferences in a particular usage context. In this paper, we present an approach for integrating recommendation and electronic map technologies to build a map-based conversational mobile recommender system that can effectively and intuitively support users in finding their desired products and services. The results of our real-user study show that integrating map-based visualization and interaction in mobile recommender systems improves the system recommendation effectiveness and increases the user satisfaction.
GENERATING COMPARATIVE DESCRIPTIONS OF PLACES OF INTEREST IN THE TOURISM DOMAIN
AUTHOR: B.D. Carolis, N. Novielli, V.L. Plantamura, and E. Gentile
PUBLISH: Proc. Third ACM Conf. Recommender Systems (RecSys ’09), pp. 277-280, 2009.
When visiting cities as tourists, most of the times people do not make very detailed plans and, when choosing where to go and what to seem they tend to select the area with the major number of interesting facilities. Therefore, it would be useful to support the user choice with contextual information presentation, information clustering and comparative explanations of places of potential interest in a given area. In this paper we illustrate how My Map, a mobile recommender system in the Tourism domain, generates comparative descriptions to support users in making decisions about what to see, among relevant objects of interest.
CHAPTER 2
EXISTING SYSTEM:
We first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes a tourist-area-season topic (TAST) model which can represent travel packages and tourists by different topic distributions. In the TAST model, the extraction of topics is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes.
As a result, the TAST model can well represent the content of the travel packages and the interests of the tourists. Based on this TAST model, a cocktail approach is developed for personalized travel package recommendation by considering some additional factors including the seasonal behaviors of tourists, the prices of travel packages, and the cold start problem of new packages. Finally, the experimental results on real-world travel data show that the TAST model can effectively capture the unique characteristics of travel data and the cocktail recommendation approach performs much better than traditional techniques.
DISADAVANTAGES:
PROPOSED SYSTEM:
We propose the tourist-relation-area-season topic (TRAST) model, which helps understand the reasons why tourists form a travel group. This goes beyond personalized package recommendations and is helpful for capturing the latent relationships among the tourists in each travel group. In addition, we conduct systematic experiments on the real world data. These experiments demonstrate that the TRAST model can be used as an assessment for travel group automatic formation but also provide more insights into the TAST model and the cocktail recommendation approach.
Our contributions of the cocktail approaches, and the TRAST model for travel package recommendations are each dashed rectangular box in the dashed circle identifies a travel group and the tourists in the same travel group are represented in this TRAST model, all the tourists’ travel preferences are represented by relationship distributions. For a set of tourists, who want to travel the same package, we can use their relationship distributions as features to cluster them, so as to put them into different travel groups. Thus, in this scenario, many clustering methods can be adopted. Since choosing clustering algorithm is beyond the scope of this paper, in the experiments, we refer to K-means one of the most popular clustering algorithms.
ADVANTAGES:
HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
SYSTEM DESIGN
SYSTEM ARCHITECTURE:
ARCHITECTURE DIAGRAM:
CHAPTER 4
DATA FLOW DIAGRAM:
UML DIAGRAMS
UML stands for Unified Modeling Language. UML is a standardized general-purpose modeling language in the field of object-oriented software engineering. The standard is managed, and was created by, the Object Management Group.
The goal is for UML to become a common language for creating models of object oriented computer software. In its current form UML is comprised of two major components: a Meta-model and a notation. In the future, some form of method or process may also be added to; or associated with, UML.
The Unified Modeling Language is a standard language for specifying, Visualization, Constructing and documenting the artifacts of software system, as well as for business modeling and other non-software systems.
The UML represents a collection of best engineering practices that have proven successful in the modeling of large and complex systems.
The UML is a very important part of developing objects oriented software and the software development process. The UML uses mostly graphical notations to express the design of software projects.
GOALS:
The Primary goals in the design of the UML are as follows:
USE CASE DIAGRAM:
A use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals (represented as use cases), and any dependencies between those use cases. The main purpose of a use case diagram is to show what system functions are performed for which actor. Roles of the actors in the system can be depicted.
CLASS DIAGRAM:
In software engineering, a class diagram in the Unified Modeling Language (UML) is a type of static structure diagram that describes the structure of a system by showing the system’s classes, their attributes, operations (or methods), and the relationships among the classes. It explains which class contains information.
SEQUENCE DIAGRAM:
A sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram that shows how processes operate with one another and in what order. It is a construct of a Message Sequence Chart. Sequence diagrams are sometimes called event diagrams, event scenarios, and timing diagrams.
ACTIVITY DIAGRAM:
Activity diagrams are graphical representations of workflows of stepwise activities and actions with support for choice, iteration and concurrency. In the Unified Modeling Language, activity diagrams can be used to describe the business and operational step-by-step workflows of components in a system. An activity diagram shows the overall flow of control.
MODULES:
COCKTAIL APPROACH
TRAVEL PACKAGE
TRAST MODEL
RECOMMENDATIONS
MODULES DESCRIPTION:
COCKTAIL APPROACH:
Our cocktail approaches the specific preferences of the tourist while he/she is planning a trip, such as the transportation preference. In other words, we focus on designing the recommendation algorithm to attract the tourists before they make a travel decision rather than providing the travel support in the on-tour stage. Thus, our approach may be only useful in some situations (e.g., email marketing). Also, if we want to deploy this work for real-world services, tourist package and locating in the top of the recommendation list to a lack of interest of those packages, relevant (desirable) travel packages in the test set may be just a small fraction of the entire relevant ones that are actually of interest to each tourist.
TRAVEL PACKAGE:
We aim to make personalized travel package recommendations for the tourists. Thus, the users
are the tourists and the items are the existing packages, and we exploit a real-world travel data set provided by a travel company packages in a way that each tourist has traveled at least two different packages.
First, it is very sparse, and each tourist has only a few travel records. The extreme sparseness of the data leads to difficulties for using traditional recommendation techniques, such as collaborative filtering. For example, it is hard to find the credible nearest neighbors for the tourists because there are very few cotraveling packages.
Second, the travel data has strong time dependence. The travel packages often have a life cycle along with the change to the business demand, i.e., they only last for a certain period. In contrast, most of the landscapes will still be active after the original package has been discarded. These landscapes can be used to form new packages together with some other landscapes. Thus, we can observe that the landscapes are more sustainable and important than the package itself.
Third, landscape has some intrinsic features like the geographic location and the right travel seasons. Only the landscapes with similar spatial-temporal features are suitable for the same packages, i.e., the landscapes in one package have spatial-temporal autocorrelations and follow the first law of geography-everything is related to everything else, but the nearby things are more related than distant things.
Fourth, the tourists will consider both time and financial costs before they accept a package. This is quite different from the traditional recommendations where the cost of an item is usually not a concern. Thus, it is very important to profile the tourists based on their interests as well as the time and the money they can afford. Since the package with a higher price often tends to have more time and vice versa, in this paper we only take the price factor into consideration.
Fifth, people often travel with their friends, family, or colleagues. Even when two tourists in the same travel group are totally strangers, there must be some reasons for the travel company to put them together. For instance, they may be of the same age or have the same travel schedule. Hence, it is also very important to understand the relationships among the tourists in the same travel group. This understanding can help to form the travel group. Last but not least, few tourist ratings are available for travel packages. However, we can see that every choice of a travel package indicates the strong interest of the tourist in the content provided in the package.
TRAST MODEL:
TRAST model, all the tourists’ travel preferences are represented by relationship distributions. For a set of tourists, who want to travel the same package, we can use their relationship distributions as features to cluster them, so as to put them into different travel groups. Thus, in this scenario, many clustering methods can be adopted. Since choosing clustering algorithm is beyond the scope of this paper, in the experiments, we refer to K-means, one of the most popular clustering algorithms. Thus, the TRAST model can be used as an assessment for travel group automatic formation. Indeed, in real applications, when generating a travel group, some more external constraints, such as tourists’ travel date requirements, the travel company’s travel group schedule should also be considered by TRAST1 to represent the latent relationships directly.
TRAST model, the purchases of the tourists in each travel group are summed up as one single expense record and, thus, it has more complex generative process. We can understand this process by a simple example. Assume that two selected tourists in a travel group (U00d) are u1 and u2, who are young and dating with each other. Now, they decide to travel in winter (Sd) and the destination is North America (Ad). To generate a travel landscape (l), we first extract a relationship (r, e.g., lover), and then find a topic (t) for lovers to travel in the winter (e.g., skiing). Finally, based on this skiing topic and the selected travel area (e.g., Northeast America), we draw a landscape (e.g., Stowe, Vermont).
RECOMMENDATIONS:
The evaluations in previous sections are mainly focused on the individual (personalized) recommendations. Since there are tourists who frequently travel together, it is interesting to know whether the latent variables (e.g., the topics of each individual tourist and the relationships of a travel group) as well as the cocktail approaches are useful for making recommendations to a group of tourists. To this end, we performed an experimental study on group recommendations.
We
adopt the widely used degree of agreement (DOA) and Top-K [23] as the
evaluation metrics. Also, a simple user study was conducted and volunteers were
invited to rate the recommendations. For comparison, we recorded the best performance
of each algorithm by tuning their parameters, and we also set some general
rules for fair comparison. For instance, for collaborative filtering-based
methods, we usually consider the contribution of the nearest neighbors with
similarity values larger than 0.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The
aspect of study is to check the level of acceptance of the system by the user.
This includes the process of training the user to use the system efficiently.
The user must not feel threatened by the system, instead must accept it as a
necessity. The level of acceptance by the users solely depends on the methods
that are employed to educate the user about the system and to make him familiar
with it. His level of confidence must be raised so that he is also able to make
some constructive criticism, which is welcomed, as he is the final user of the
system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months. This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.
5.2.1 UNIT TESTING:
A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program testing checks for two types of errors: syntax and logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.
UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
5.1.3 FUNCTIONAL TESTING:
Functional testing of an application is used to
prove the application delivers correct results, using enough inputs to give an
adequate level of confidence that will work correctly for all sets of inputs.
The functional testing will need to prove that the application works for each
client type and that personalization function work correctly.When a
program is tested, the actual output is compared with the expected output. When
there is a discrepancy the sequence of instructions must be traced to determine
the problem. The process is facilitated
by breaking the program into self-contained portions, each of which can be
checked at certain key points. The idea is to compare program values against
desk-calculated values to isolate the problems.
FUNCTIONAL TESTING:
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 4 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.5 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
Load Testing
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
PERFORMANCE TESTING:
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
RELIABILITY TESTING:
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics
that relate to the availability, integrity and confidentiality of the system
data and services. Users/Clients should be encouraged to make sure their
security needs are very clearly known at requirements time, so that the
security issues can be addressed by the designers and testers.
SECURITY TESTING:
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.7 WHITE BOX TESTING:
White
box testing, sometimes called glass-box
testing is a test case
design method that uses
the control structure
of the procedural design to
derive test cases. Using
white box testing
method, the software engineer
can derive test
cases. The White box testing focuses on the inner structure of the
software structure to be tested.
5.1.8 WHITE BOX TESTING:
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing,
focuses on the functional requirements of the software. That
is, black testing
enables the software
engineer to derive
sets of input
conditions that will
fully exercise all
functional requirements for a
program. Black box testing is not
alternative to white box techniques.
Rather it is
a complementary approach
that is likely
to uncover a different
class of errors
than white box methods. Black box testing attempts to find
errors which focuses on inputs, outputs, and principle function of a software
module. The starting point of the black box testing is either a specification
or code. The contents of the box are hidden and the stimulated software should
produce the desired results.
5.1.10 BLACK BOX TESTING:
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All the above system testing strategies
are carried out in as the development, documentation and institutionalization
of the proposed goals and related policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
Java technology is both a programming language and a platform.
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
6.5 ODBC:
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages of this scheme are so numerous that you
are probably thinking there must be some catch. The only disadvantage of ODBC
is that it isn’t as efficient as talking directly to the native database
interface. ODBC has had many detractors make the charge that it is too slow.
Microsoft has always claimed that the critical factor in performance is the
quality of the driver software that is used. In our humble opinion, this is
true. The availability of good ODBC drivers has improved a great deal recently.
And anyway, the criticism about performance is somewhat analogous to those who
said that compilers would never match the speed of pure assembly language.
Maybe not, but the compiler (or ODBC) gives you the opportunity to write
cleaner programs, which means you finish sooner. Meanwhile, computers get
faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
6.7 JDBC Goals:
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
Because
more often than not, the usual SQL calls used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
A
socket is a data structure maintained by the system to handle network
connections. A socket is created using the call socket. It returns an integer that is like a file
descriptor. In fact, under Windows, this handle can be used with Read File and Write File functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here
“family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus default implementation), a rendered, and integrating this with the existing XYPlot class in JFreeChart; Testing, documenting, testing some more, documenting some more.
6.8.2. Time Series Chart Interactivity
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The
property editor mechanism in JFreeChart only handles a small subset of the
properties that can be set for charts. Extend (or reimplement) this mechanism
to provide greater end-user control over the appearance of the charts.
CHAPTER 7
APPENDIX
7.1 SAMPLE SOURCE CODE
7.2
SAMPLE OUTPUT
CHAPTER 8
CONCLUSION:
We present study on personalized travel package recommendation. Specifically, we first analyzed the unique characteristics of travel packages and developed the TAST model, a Bayesian network for travel package and tourist representation. The TAST model can discover the interests of the tourists and extract the spatial-temporal correlations among landscapes. Then, we exploited the TAST model for developing a cocktail approach on personalized travel package recommendation. This cocktail approach follows a hybrid recommendation strategy and has the ability to combine several constraints existing in the real-world scenario.
Furthermore,
we extended the TAST model to the TRAST model, which can capture the
relationships among tourists in each travel group. Finally, an empirical study
was conducted on real-world travel data. Experimental results demonstrate that
the TAST model can capture the unique characteristics of the travel packages,
the cocktail approach can lead to better performances of travel package recommendation,
and the TRAST model can be used as an effective assessment for travel group
automatic formation. We hope these encouraging results could lead to many
future works.