In the recent of social media, sentiment analysis has developed rapidly in recent years. However, only a few studies focused on the field of transportation, which failed to meet the stringent requirements of safety, efficiency, and information exchange of intelligent transportation systems (ITSs). We propose the traffic sentiment analysis (TSA) as a new tool to tackle this problem, which provides a new prospective for modern ITSs.
Our methods and models in TSA are
proposed in this paper, and the advantages and disadvantages of rule- and
learning-based approaches are analyzed based on web data. Practically, we
applied the rule-based approach to deal with real problems, presented an architectural
design, constructed related bases, demonstrated the process, and discussed the
online data collection.
1.2 INTRODUCTION
Transportation systems serve the people in essence, but the modern intelligent transportation systems (ITSs) failed to concern about the public opinions. For the completeness of ITS space, it is necessary to collect and analyze the public wisdom and opinions. With the remarkable advancement of Web 2.0 in the last decade, communication platforms, such as blogs, wikis, online forums, and social-networking groups, have become a rich data-mining source for the detection of public opinions. Therefore, we propose traffic sentiment analysis (TSA) for processing traffic information from websites. As taking consideration of human affection, TSA will enrich the performance of the current ITS space.
TSA is a subfield of sentiment analysis, which concerns about the issues of traffic in particular. Due to the field sensitivity of sentiment analysis, it is necessary to discuss the TSA problems and construct TSA systems specifically. The TSA treats the traffic problems in a new angle, and it supplements the capabilities of current ITS systems. Fig. 1 illustrates the modules of ITS and exhibits that the TSA plays the role of sensing, computing, and supporting the decision making in ITSs.
The functions of the TSA system can be illustrated as follows.
1) Investigation: It is more economical and efficient than the public poll to collect the public opinion through the TSA system.
2) Evaluation: The computational production of the TSA system can be used to evaluate the performance of traffic services and policies.
3)
Prediction: The TSA system can be further developed
to predict the trends of some social events. For example, to predict whether a
cancelled flight would bring chaos, we can analyze the emotion of passengers on
their words published on Twitter or Weibo through TSA systems.
In addition, specific parts of the TSA system can be viewed as another form of “social sensors” compared with traditional sensor systems; it can detect the situation from a new humanized perspective. The TSA system is independent of current systems, which is particularly useful in an emergency when other systems were ruined. For example, in 2009, the volcano ash from Iceland caused the malfunction of many cameras in several European countries. In this paper, by constructing a specific TSA system, we addressed the issues and methods in this field and illustrated two cases to demonstrate the value of this research.
Our contribution in this paper can be addressed as follows.
1) We proposed TSA to view the traffic problems in a new perspective.
2) The main issues of TSA applications on web data were discussed based on the web data.
3) The key problems of TSA were
addressed, including the design of architecture, the improved rule-based approach,
and the construction of related bases.
1.3 LITRATURE SURVEY
CHINESE WORD SEGMENTATION FOR TERRORISM-RELATED CONTENTS
PUBLICATION: D. Zeng, D. Wei, M. Chau, and F. Wang, Intelligence and Security Informatics.New York, NY, USA: Springer-Verlag, 2008, pp. 1–13.
EXPLANATION:
In order to analyze
security and terrorism related content in Chinese, it is important to perform
word segmentation on Chinese documents. There are many previous studies on
Chinese word segmentation. The two major approaches are statistic-based and
dictionary-based approaches. The pure statistic methods have lower precision,
while the pure dictionary-based method cannot deal with new words and are
restricted to the coverage of the dictionary. In this paper, we propose a
hybrid method that avoids the limitations of both approaches. Through the use
of suffix tree and mutual information (MI) with the dictionary, our segmenter,
called IASeg, achieves a high accuracy in word segmentation when domain
training is available. It can identify new words through MI-based token merging
and dictionary update. In addition, with the Improved Bigram method it can also
process N-grams. To evaluate the performance of our segmenter, we compare it
with the Hylanda segmenter and the ICTCLAS segmenter using a terrorism-related
corpus. The experiment results show that IASeg performs better than the two
benchmarks in both precision and recall.
AGENT-BASED CONTROL FOR NETWORKED TRAFFIC MANAGEMENT SYSTEMS
PUBLICATION: F.-Y. Wang, IEEE Intell. Syst., vol. 20, no. 5, pp. 92–96, Sep./Oct. 2005.
EXPLANATION:
Agent or multiagent
systems have evolved and diversified rapidly since their inception around the
mid 1980s as the key concept and method in distributed artificial intelligence.
They have become an established, promising research and application field
drawing on and bringing together results and concepts from many disciplines,
including AI, computer science, sociology, economics, organization and
management science, and philosophy. However, multiagent systems have yet to
achieve widespread use for controlling traffic management systems. Most
research focuses on developing hierarchical structures, analytical modeling,
and optimized algorithms that are effective for real-time traffic applications,
as you can see from well-known traffic control systems such as CRONOS, OPAC,
SCOOT, SCAT, PRODYN, and RHODES. Although those functional-decomposition-based
systems are useful and successful for many traffic management problems, costs
and difficulties associated with their development, operation, maintenance,
expansion, and upgrading are often prohibitive and sometimes unnecessary,
especially in the rapidly arriving age of connectivity. We need to rethink
control systems and reinvestigate the use of simple task-oriented agents for
traffic control and management of transportation systems.
OPINION FEATURE EXTRACTION USING CLASS SEQUENTIAL RULES
PUBLICATION: M. Hu and B. Liu, presented at the AAAI Spring Symposium Computational
Approaches Analyzing Weblogs, Palo Alto, CA, USA, 2006, Paper AAAI-CAAW-06.
EXPLANATION:
The paper studies the problem of
analyzing user comments and reviews of products sold online. Analyzing such
reviews and producing a summary of them is very useful to both potential
customers and product manufacturers. By analyzing reviews, we mean to extract
features of products (also called opinion features) that have been commented by
reviewers and determine whether the opinions are positive or negative. This
paper focuses on extracting opinion features from Pros and Cons, which
typically consist of short phrases or incomplete sentences. We propose a
language pattern based approach for this purpose. The language patterns are
generated from Class Sequential Rules (CSR). A CSR is different from a classic
sequential pattern because a CSR has a fixed class (or target). We propose an
algorithm to mine CSR from a set of labeled training sequences. To perform
extraction, the mined CSRs are transformed into language patterns, which are
used to match Pros and Cons to extract opinion features. Experimental results
show that the proposed approach is very effective.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Existing approaches to sentiment analysis can be categorized into rule- and learning based approaches. Rule-based approaches often require an expert-defined dictionary of subjective words; this approach predicts the polarity of a sentence or document by analyzing the occurring patterns of such words in text. For example, Wiebe et al. provided a lexicon source of subjectivity clues, such as verbs, adjectives, and nouns, with their polarity (i.e., positive, negative, or neutral) and strength (i.e., strong or weak) annotated. However, this lexicon is able to define the original polarity of a word only, and the actual polarity of a word may be modified by its context in a sentence. Several approaches that consider the context of words have been proposed to determine the sentiment orientation of words.
Previous studies, the data set contains
several subjective texts that could not be easily analyzed by the rules. The
most typical phenomenon is the ironic sentiment sentences. For instance, in
posts regarding fuel prices, the thread title used was “the fuel price will
rise,” to which one user replied, “go to sell the car.” Such a reply apparently
carries an ironic tone; thus, all annotators manually labeled the reply as
“negative.” However, given that the computer cannot detect from the given text
any word expressing a negative sentiment, the methods cannot recognize the sentiment
polarity. Therefore, numerous problems remain unsolved.
2.1.1 DISADVANTAGES:
2.2 PROPOSED SYSTEM:
We propose traffic sentiment analysis (TSA) for processing traffic information from websites. As taking consideration of human affection, TSA will enrich the performance of the current ITS space. TSA is a subfield of sentiment analysis, which concerns about the issues of traffic in articular. Due to the field sensitivity of sentiment analysis, it is necessary to discuss the TSA problems and construct TSA systems specifically.
The TSA treats the traffic problems in a new angle, and it supplements the capabilities of current ITS systems in the modules of ITS and exhibits that the TSA plays the role of sensing, computing, and supporting the decision making in ITSs. The functions of the TSA system can be illustrated as follows. 1) Investigation: It is more economical and efficient than the public poll to collect the public opinion through the TSA system. 2) Evaluation: The computational production of the TSA system can be used to evaluate the performance of traffic services and policies. 3) Prediction: The TSA system can be further developed to predict the trends of some social events.
For example, to predict whether a
cancelled flight would bring chaos, we can analyze the emotion of passengers on
their words published on Twitter or Weibo through TSA systems. In addition,
specific parts of the TSA system can be viewed as another form of “social
sensors” compared with traditional sensor systems; it can detect the situation
from a new humanized perspective.
2.2.1 ADVANTAGES:
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.
MODELING RULES:
There are several common modeling rules when creating DFDs:
3.1 ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
3.3 CLASS DIAGRAM:
3.4 SEQUENCE DIAGRAM:
3.5
ACTIVITY DIAGRAM:
CHAPTER 4
4.0 IMPLEMENTATION:
TSA ARCHITECTURE
Previous studies on Chinese texts have devoted considerable efforts on architectural design. Che et al. designed the architecture of the language technology platform (LTP), an integrated Chinese processing platform including a suite of high-performance natural language processing (NLP) modules and relevant corpora. They achieved plausible results in several relevant evaluations, particularly for syntactic and semantic parsing modules. Li et al. designed the architecture of sentiment analysis application in the financial domain on the basis of morphemes. A rule-based approach is adopted here to address the distinct challenges posed by the Chinese data set. Fig. 2 illustrated the architecture of TSA; the architecture is based on the tackling process; and its main components, including 1) web data collection, 2) preprocessing, 3) extraction of subjects and objects, 4) extraction of sentiment properties, 5) sentiment calculation and classification, 6) evaluation or applications, and 7) feed- back, improve the construction of the sentiment, rule, and TSA object bases.
Data collection: To address the problem, we gathered data from several websites, such as Sina Weibo, Tencent Weibo, Tianya, and autohome (the upper block in Fig. 2), ensuring that the conclusions are definitely based on public opinion or, at least, represent part of the public opinion details of data collection are discussed in Section V.
Preprocessing: As previously mentioned, Chinese documents must be processed additionally because that Chinese language does not segment words by spaces in sentences. In the preprocessing, the following steps are included: 1) the segmentation of text, 2) the labeling of words, and 3) the replacement of synonymous expressions. The first two steps are done by a Chinese segmentation tool; we employ the Chinese Lexical Analysis System 3 launched by the social media, various expressions denote the same meaning. For example, several users commonly use “d,” which represents the Chinese character “ ” (support), to express agreement with others. Therefore, the replacement of synonymous expressions (step 3) is necessary to reduce the complexity and increase the precision of following processes.
Word segmentation optimization: To avoid unnecessary disturbances and improve precision, preprocessing should be conducted according to the material and the demand of the algorithms. However, in practice, the result of word segmentation in Chinese is far from expected. In some cases, this step may even reduce the precision. For example, “” is separated as ( /n). In fact, “ ” is an abbreviation of a company name, which represents one of the two Chinese oil giants. Therefore, it is necessary to improve the performance of the Chinese segmentation. In this paper, we propose to construct the “sentiment base” in the application of TSA. In practice, the “sentiment base” consists of the TSA sentiment base and HowNet (subsection B).
Extraction of subjects and objects: Subjects
and objects are mainly extracted by context mining and document analysis. In
TSA, appropriate models should be designed in context mining according to
different data sets and resources. Context mining should obtain results as
efficiently as possible to provide the necessary background knowledge for the
subsequent steps. In practice, context mining includes conservation extraction
and coreference analysis. Conservation extraction refers to handling the text,
such as “citation, @.” In addition, coreference analysis refers to mining the
object represented by other words. For example, the address in Sina Weibo is
usually represented by a hyperlink.
4.1 ALGORITHM
In this paper, we propose to construct the sentiment, modifier, object, and rule bases. Assume that the sentiment polarity of a word is determined by its morphemes. If the morphemes of a word appear in the positive lexicon more frequently than they do in the negative lexicon, the word is positive; otherwise, the word is negative. To measure the positive and negative tendencies of the morpheme q, we assign positive and negative weights to the morphemes as follows:
In formula (3), the polarity Sci depends on morphemes Ci, and the absolute value of Sci is the degree of tendency of morphemes Ci. The steps for calculating the sentiment polarity of words are as follows. Scan the positive and negative word lexicons; if the word w appears in the positive word lexicon, Sw = 1; if the word appears in the negative word lexicon, Sw = −1. Otherwise, the sentiment polarity is computed using morphemes by
Where Sw represents the sentiment polarity of the word w, which consists of c1, c2, . . . , cp. If Sw > 0, the sentiment polarity of the word is positive; otherwise, the sentiment polarity of the word is negative. If the value obtained is close to zero, the word can be considered neutral.
4.2 MODULES:
DATA COLLECTION TSA:
IT’S TRANSPORT SYSTEMS:
RULE BASED APPROACH:
TSA
ANALYTICAL TECHNIQUE:
4.3 MODULE DESCRIPTION:
DATA COLLECTION TSA:
Information regarding traffic on the Web can be classified
into three categories. The first category consists of news, expert
commentaries, announcements, etc., from the traffic website.
The second includes posts from the transport sector in forums.
These forums provide a platform through which users
can exchange information about social topics, such as traffic
congestions and transportation policies. The third includes realtime
information about traffic in microblogging, which can be
found from the social media, such as weibo.com. The sentiment
polarity of the first category is not easily distinguished, but its
content is true and meaningful. The sentiment polarity of the
second category is clear, and usually, a discussion on certain
events or topics may be highly valuable for tracking public
opinion. The third category, which includes real-time traffic
information, may not have a fixed topic but often located in a
certain place. Such information bears significance for obtaining
real-time information of travelers and creating a backup sensor
network system. Data from the specific websites can be collected by the open
application programming interface or correspondent crawler,
such as the first and third categories of information. However,
collecting a data set on a specific topic is more difficult. In most
forums, the information-publishing platform can be divided
into a series of boards containing various categories or topics. In
a predefined subject board, the topics are designed for specific
events, providing a relatively better framework for the readers
and commenters. Nevertheless, the categorization is too simple
and indistinct for analysis and research because of the following
reasons: 1) not all topics can be mapped to a single board; 2) the
contents of the post are not strictly related to the object topics;
and 3) a board of forum often contains more than one topic.
Therefore, to precisely collect a topic line and gather the
information to one post, we first design a special crawler by
using depth retrieval. Traffic-related terms are adopted to build
the key ontological vocabulary used for the built-in search
engine of
the website.
IT’S TRANSPORT SYSTEMS:
The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to further address the increasing transportation issues, such as heavy traffic, congestion, and vehicle safety. In the past few years, researchers have proposed a few models that use cloud computing for implementing intelligent transportation systems (ITSs). For example, a new vehicular cloud architecture called ITS-Cloud was proposed to improve vehicle-to-vehicle communication and road safety A cloud-based urban traffic control system was proposed to optimize traffic control a service-oriented architecture (SOA), this system uses a number of software services (SaaS), such as intersection control services, area management service, cloud service discovery service, and sensor service, to perform different tasks.
These services also interact with each other to exchange information and provide a solid basis for building a collaborative traffic control and processing system in a distributed cloud environment. As an emerging technology caused by rapid advances in modern wireless telecommunication, IoT has received a lot of attention and is expected to bring benefits to numerous application areas including health care, manufacturing, and transportation. Currently, the use of IoT in transportation is still in its early stage and most research on ITSs has not leveraged the IoT technology as a solution or an enabling infrastructure.
We propose to use both cloud computing and IoT as an
enabling infrastructure for developing a vehicular data cloud platform where
transportation-related information, such as traffic control and management, car
location tracking and monitoring, road condition, car warranty, and maintenance
information, can be intelligently connected and made available to drivers,
automakers, part-manufacturer, vehicle quality controller, safety authorities,
and regional transportation division. An experiment of using data mining models
to analyze vehicular data clouds in the IoT environment was also conducted to
demonstrate the feasibility of vehicular data mining service.
RULE BASED APPROACH:
Rule-based approach is needed, e.g., whether a noun that could represent the sentiment of the texts exists. As emphasized in previous studies, the data set contains several subjective texts that could not be easily analyzed by the rules. The most typical phenomenon is the ironic sentiment sentences. For instance, in posts regarding fuel prices, the thread title used was “the fuel price will rise,” to which one user replied, “go to sell the car.” Such a reply apparently carries an ironic tone; thus, all annotators manually labeled the reply as “negative.” However, given that the computer cannot detect from the given text any word expressing a negative sentiment, the methods cannot recognize the sentiment polarity. Therefore, numerous problems remain unsolved. For the limitations of the existing lexicons, an improved lexicon should be developed, which requires long-term and arduous efforts. We proposed the construction of ITSs under the architecture of artificial, computational, and parallel (ACP) methods, with the TSA system as one of the data sources.
TSA ANALYTICAL TECHNIQUE:
Text sentiment calculation can be categorized into three levels, namely, word, sentence, and document levels. The calculation of the sentiment polarity of words is a basic step in the construction of the sentiment word base. In practice, we consider the words or phrases as another form of sentence. Therefore, text processing includes two main parts, the polarity calculation of the sentence- and document-level text. Fig. 3 shows the overall process involved in the proposed approach. The method includes two major steps, i.e., the sentence sentiment analysis and document sentiment aggregation. Considering the subtlety of Chinese expression, we first decompose a document into constituting sentences and determine the sentiment polarity of each sentence. In contrast to early document-level analytical approaches we regard sentences as atomic units for semantic analysis. The polarity scores of all the sentences are subsequently synthesized to compute for the overall polarity of the entire document. The sentiment polarity of a sentence is defined as ps. ps is determined to extract the SND patterns and calculate the sentiment polarity score according to the SND patterns identified in the text.
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:
| 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. |
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.
5.1.2 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.
| 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. 3 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:
5.1.4 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.
| 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.
| 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.
| 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.
| 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. |
5.1.8 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.
| 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.
| 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:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
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.
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.
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
CHAPTER 8
8.1 CONCLUSION
We have proposed Web-based TSA to analyze the traffic problems in a humanizer way. To the best of our knowledge, this is the first attempt to apply sentiment analysis on the area of traffic. The study of TSA will provide us a new perspective when facing with traffic problems.
Our work can be concluded as the following five folds: 1) designing the application architecture of TSA; 2) constructing the related bases for the TSA system; 3) comparing the advantages and disadvantages of both rule- and learning-based approaches based on the characters of web data; 4) proposing an algorithm for the sentiment polarity calculation based on the rule-based approach; and 5) taking consideration of the modifying relationships of sentence patterns and locations in the sentiment polarity calculations.
The task to implement the TSA system
into existing ITSs is also critically important, and it does need further
research. We suggested that take the policy evaluation part to support decision
making of managers and view the evaluation results related to specific location
as sensor information. The keynote of implementation is jointly accommodating
the traveler’s best interest and reasonable workload. Since TSA is still in its
infancy, we anticipate that more techniques will be developed for the joint
performance of ITS with the TSA system in the future.