A Cocktail Approach for Travel Package Recommendation

A COCKTAIL APPROACH FOR TRAVEL PACKAGE RECOMMENDATION

ABSTRACT:

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.

EXISTING SYSTEM:

 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 are usually developed for different travel seasons. Therefore, the landscapes in a travel package usually have spatial temporal 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 value 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.

DISADVANTAGES OF EXISTING SYSTEM:

·  The problem of leveraging unique features to distinguish personalized travel package recommendations from traditional recommender systems remains pretty open.

·       The user ratings are usually not conveniently available.

PROPOSED SYSTEM:

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, we develop 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.

ADVANTAGES OF PROPOSED SYSTEM:

·       It goes beyond personalized package recommendations and is helpful for capturing the latent relationships among the tourists in each travel group.

·       It aims to make personalized travel package recommendations for the tourists.

SYSTEM CONFIGURATION:

HARDWARE REQUIREMENTS:

ü Processor                  -        Pentium –IV

ü Speed                        -        1.1 Ghz

ü RAM                         -        512 MB(min)

ü Hard Disk                 -        40 GB

ü Key Board                -        Standard Windows Keyboard

ü Mouse                       -        Two or Three Button Mouse

ü Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

Operating system      :         Windows XP.

Coding Language      :         .Net

Data Base                 :         SQL Server 2005

Tool                          :         VISUAL STUDIO 2008.

REFERENCE:

Qi Liu, Enhong Chen, Hui Xiong, Yong Ge, Zhongmou Li, and Xiang Wu, “A Cocktail Approach for Travel Package Recommendation”  IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 2, FEBRUARY 2014.

Enabling Data Integrity Protection in Regenerating-Coding-Based Cloud Storage: Theory and Implementation

ENABLING DATA INTEGRITY PROTECTION IN REGENERATING-CODING-BASED CLOUD STORAGE: THEORY AND IMPLEMENTATION

TO VIEW OUTPUT CLICK HERE

ABSTRACT:

To protect outsourced data in cloud storage against corruptions, adding fault tolerance to cloud storage, along with efficient data integrity checking and recovery procedures, becomes critical. Regenerating codes provide fault tolerance by striping data across multiple servers, while using less repair traffic than traditional erasure codes during failure recovery. Therefore, we study the problem of remotely checking the integrity of regenerating-coded data against corruptions under a real-life cloud storage setting. We design and implement a practical data integrity protection (DIP) scheme for a specific regenerating code, while preserving its intrinsic properties of fault tolerance and repair-traffic saves. Our DIP scheme is designed under a mobile Byzantine adversarial model, and enables a client to feasibly verify the integrity of random subsets of outsourced data against general or malicious corruptions. It works under the simple assumption of thin-cloud storage and allows different parameters to be fine-tuned for a performance-security trade-off. We implement and evaluate the overhead of our DIP scheme in a real cloud storage testbed under different parameter choices. We further analyze the security strengths of our DIP scheme via mathematical models. We demonstrate that remote integrity checking can be feasibly integrated into regenerating codes in practical deployment.

EXISTING SYSTEM:

One major use of cloud storage is long-term archival, which represents a workload that is written once and rarely read. While the stored data are rarely read, it remains necessary to ensure its integrity for disaster recovery or compliance with legal requirements. Since it is typical to have a huge amount of archived data, whole-file checking becomes prohibitive. Proof of retrievability (POR)  and proof of data possession (PDP)  have thus been proposed to verify the integrity of a large file by spot checking only a fraction of the file via various cryptographic primitives.

DISADVANTAGES OF EXISTING SYSTEM:

v Data have been accidentally corrupted or maliciously compromised by insider/outsider attacks..

v Security concerns arise when data storage is outsourced to third party cloud storage providers.

v Data corrupted due to server failures.

PROPOSED SYSTEM:

we design and implement a practical data integrity protection (DIP) scheme for regenerating-codingbased cloud storage. We augment the implementation of functional minimum-storage regenerating (FMSR) codes and construct FMSR-DIP codes, which allow clients to remotely verify the integrity of random subsets of long-term archival data under a multiserver setting. FMSR-DIP codes preserve fault tolerance and repair traffic saving as in FMSR codes. Also, we assume only a thin-cloud interface, meaning that servers only need to support standard read/ write functionalities. This adds to the portability of FMSRDIP codes and allows simple deployment in general types of storage services. By combining integrity checking and efficient recovery, FMSR-DIP codes provide a low-cost solution for maintaining data availability in cloud storage.

ADVANTAGES OF PROPOSED SYSTEM:

v FMSR-DIP codes, which enable integrity protection, fault tolerance, and efficient recovery for cloud storage.

v Using several cryptographic primitives.

v Lost data founded easily.

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

Processor                  -        Pentium –IV

Speed                        -        1.1 Ghz

RAM                         -        512 MB(min)

Hard Disk                 -        40 GB

Key Board                -        Standard Windows Keyboard

Mouse                       -        Two or Three Button Mouse

Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

Operating system      :         Windows XP.

Coding Language      :         .Net

Data Base                 :         SQL Server 2005

Tool                          :         VISUAL STUDIO 2008.

REFERENCE:

Henry C.H. Chen and Patrick P.C. Lee, “Enabling Data Integrity Protection in Regenerating-Coding-Based Cloud Storage: Theory and Implementation” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 25, NO. 2, FEBRUARY 2014

Consistency as a service: Auditing cloud consistency

CONSISTENCY AS A SERVICE: AUDITING CLOUD CONSISTENCY

ABSTRACT:

Cloud storage services have become commercially popular due to their overwhelming advantages. To provide ubiquitous always-on access, a cloud service provider (CSP) maintains multiple replicas for each piece of data on geographically distributed servers. A key problem of using the replication technique in clouds is that it is very expensive to achieve strong consistency on a worldwide scale. In this paper, we first present a novel consistency as a service (CaaS) model, which consists of a large data cloud and multiple small audit clouds. In the CaaS model, a data cloud is maintained by a CSP, and a group of users that constitute an audit cloud can verify whether the data cloud provides the promised level of consistency or not. We propose a two-level auditing architecture, which only requires a loosely synchronized clock in the audit cloud. Then, we design algorithms to quantify the severity of violations with two metrics: the commonality of violations, and the staleness of the value of a read. Finally, we devise a heuristic auditing strategy (HAS) to reveal as many violations as possible. Extensive experiments were performed using a combination of simulations and a real cloud deployment to validate HAS.

EXISTING SYSTEM:

Cloud storage services can be regarded as a typical service in cloud computing, which involves the delivery of data storage as a service, including database-like services and network attached storage, often billed on a utility computing basis, e.g., per gigabyte per month. Examples include Amazon SimpleDB1, Microsoft Azure storage2, and so on. By using the cloud storage services, the customers can access data stored in a cloud anytime and anywhere, using any device, without caring about a large amount of capital investment when deploying the underlying hardware infrastructures.

DISADVANTAGES OF EXISTING SYSTEM:

1. Very Expensive.
2. User cannot see the latest updates.
3. Traffic enables during the updating of new one.

PROPOSED SYSTEM:

In cloud storage, consistency not only determines correctness but also the actual cost per transaction. In this paper, we present a novel consistency as a service (CaaS) model for this situation. The CaaS model consists of a large data cloud and multiple small audit clouds. The data cloud is maintained by a CSP, and an audit cloud consists of a group of users that cooperate on a job, e.g., a document or a project. A service level agreement (SLA) will be engaged between the data cloud and the audit cloud, which will stipulate what level of consistency the data cloud should provide, and how much (monetary or otherwise) will be charged if the data cloud violates the SLA.

ADVANTAGES OF PROPOSED SYSTEM:

1.  Audit cloud is identified by a unique ID.
2.  User can read and revise data’s at anywhere.
3.  Less Expensive.

SYSTEM ARCHITECTURE:

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

Processor                  -        Pentium –IV

Speed                        -        1.1 Ghz

RAM                         -        512 MB(min)

Hard Disk                 -        40 GB

Key Board                -        Standard Windows Keyboard

Mouse                       -        Two or Three Button Mouse

Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

Operating system      :         Windows XP.

Coding Language      :         .Net

Data Base                 :         SQL Server 2005

Tool                          :         VISUAL STUDIO 2008.

REFERENCE:

Qin Liu, Guojun Wang, Member, IEEE, and Jie Wu, Fellow, IEEE., “Consistency as a Service: Auditing Cloud Consistency” IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, VOL. 11, NO. 1, MARCH 2014