Query Request Research Articles

Integration Of Big Data And Cloud Computing To Detect Black Money Check Rotation With Range Aggregate Queries

A Cloud is expanding from application aggregation and sharing to data aggregation and utilization. To make use of data tens of terabytes and tens of beta bytes of data to be handled. These massive amounts of data are called as a big data. Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Fast RAQ first divides big data into independent partitions with a balanced partitioning algorithm, and then generates a local estimation sketch for each partition. When a range-aggregate query request arrives, Fast RAQ obtains the result directly by summarizing local estimates from all partitions & Collective Results are provided. Data Mining can process only Structured Data only. Big Data Approach is spoken all over the Paper. They insist of Three Tier Architecture, 1. Big Data implementation in Multi System Approach, 2. Application Deployment - Banking / Insurance. 3. Extraction of Useful information from Unstructured Data. We implement this Project for Banking Domain. There will be Two Major Departments. 1. Bank Server for Adding New Clients and maintaining their Accounts. Every User while Registration has to provide their aadhar card as a ID Proof to create Account in any Bank. 2. Accounts Monitoring Sever will monitor every users and their Account Status in different Banks. This Server will retrieve users who maintain & Transact more than Rs. 50,000 / Annum in all 3 Accounts in different Banks using the same ID Proof. Map & Reduce is achieved.

A Real-time scheduling algorithm for on-demand wireless XML data broadcasting

Extensive Markup Language (XML) has been widely used to exchange and storage mass data in mobile computing environment. The existing research focuses on preprocessing XML document and index building, but there are little research relating to how to arrange the scheduling contents and the broadcasting sequence. This paper puts forward a real-time scheduling algorithm for on-demand wireless XML data broadcasting – XML-RxW/L to reduce the request drop ratio, average accessing time and average tuning time. Our work can be divided into the three parts: (1) XML-RxW/L is proposed to reduce the request drop ratio and average accessing time. (2) An index strategy is proposed to reduce the average tuning time by forecasting data item queue. A dynamic adjusting method on the index cycle length of XML wireless data broadcasting (X_DAIL) is proposed to determine the proper index cycle dynamically. (3) A pruning algorithm (X_Prune) of XML document is proposed to remove redundant information of XML documents. An index-building algorithm (X_Index) is proposed based on DataGuide index structure to upgrade the response speed and the accuracy rate of query request. Extensive experimental results show that the XML-RxW/L algorithm has better performance than other state-of-the-art scheduling algorithms on a real data set and a synthetic data set. A real case study by R3 system shows that the proposed algorithm presents an improvement at almost 10% than other comparable algorithms in request drop ratio (RDR), smaller at almost 6–10 s in average accessing time (AAT); and smaller at almost 12s in average tuning time (ATT).

Research on Distributed Data Storage based on Information Processing Cloud

Abstract Sensor network is a data-centric network, which provides data collection, storage and query services. Data storage and query is one of the hot spot in the research of sensor networks. In order to solve the problem of low efficiency of storage and query, high energy consumption in sensor networks, we put forward a scheme that storing distributed data of wireless sensor network based on information processing cloud. Information processing cloud is made up of a group of sensor nodes around the network center, which have the ability to absorb and process data from other nodes of sensor network which do not belong to the information processing cloud. The group of sensor nodes around the network center respond data query requests from anywhere of the network, and sensor nodes can be adjusted dynamically according to real situation, the cloud of nodes and non-cloud of nodes can be dynamically transformed as well. When non-cloud of sensor nodes store data or send query request, they only need to do centripetal movement, centrifugal movement or circumfusing movement. The analysis shows that the proposed scheme can simplify route algorithm of data storage and data query, and it also has less computation cost and storage cost than the existing schemes.

Keyword search protocol with privacy preservation using ID-based proxy reencryption

The data owner encrypts the data stored in the cloud to protect data privacy. However, when it comes to user revocation, the data owner must re-encrypt all data in order to prevent revoked users from decrypting the ciphertext. To reduce the computational cost of the data owner while simultaneously supporting privacy preservation, this paper proposes an efficient keyword search scheme by using identity ID-based proxy re-encryption technology. When a user sends a query request to the cloud server, the cloud server returns the corresponding query results encrypted by using a proxy re-encryption algorithm. Effective user revocation is achieved by simply managing re-encryption keys rather than re-encrypting the entirety of the data. Additionally, the proposed scheme supports preservation of privacy in query results. Analytical results demonstrate that the proposed protocol significantly reduces the computation and communication costs of the data owner while achieving secure user revocation.

AFZRP: Adaptive Fidelity Energy Efficient Algorithm for Zone Routing Protocols in MANET

In Mobile ad-hoc Network, routing is a challenging task due to load balance, traffic and network size and node mobility. Loads on the nodes appear in two aspects: energy consumption and traffic consumption. Energy consumed for the basic processes such as transmitting/receiving data, processing query requests, and forwarding queries/data to neighboring nodes are essential, whereas energy ingesting for the tasks like idle listening to the media, retransmitting due to packet collisions, overhearing and generating/handling control packets are not necessary and can be minimized to the least possible. This research proposes an Adaptive fidelity energy efficient algorithm for zone routing protocol to decrease the energy consumption reasonably for node activities and to prolong the network lifetime. This algorithm saves energy by keeping the node in snooze schedule mode based on the traffic load on the network. The nodes near the destination node are put into sleep state with lesser probability and away from the destination are having with the higher probability to sleep. Here, the probability of peripheral node selection depends on the energy status of the node, where the packet is destined and also peripheral node have higher wakeup interval than the normal node to ensure the availability to forward packets. And lesser wake up intervals for lightly loaded nodes to prolong the network lifetime. The proposed algorithm is suitable for nodes with higher mobility that causes for frequent changes in the network topology. The experimental results clearly show the advantages of the proposed routing algorithm for MANET. The performance of the parameters such as “per node energy consumption”, “packet delivery ratio” and overhead is analyzed using NS-2.34 Simulation tool.

LS-AMS: An Adaptive Indexing Structure for Realtime Search on Microblogs

Indexing microblogs for realtime search is challenging, because new microblogs are created at tremendous speed, and user query requests keep constantly changing. To guarantee user obtain complete query results, micro-blogging site maintains huge indices which leads to index fragmentation or extra merging overhead during realtime search. This paper proposes an efficient LogStructured index structure with Adaptive Merging Strategy (LS-AMS) for realtime search on microblogs. LS-AMS structure consists of an inverted index buffer and a sequence of dynamically adjustable index packages with exponentially increasing sizes. These index packages manage their inverted indices using adaptive merging strategy, which can reduce the merging overhead to improve query performance and can adjust the index structure based on environmental factors, such as the arrival rate of query requests and new microblogs. Experimental results show that LS-AMS can greatly improve query performance without increasing the update cost and improve the self-adaptability in dynamic environment.

Impacts of Selfish Behaviors on the Scalability of Hybrid Client–Server and Peer-to-Peer Caching Systems

This paper considers a hybrid peer-to-peer (p2p) system, a dynamic distributed caching system with an authoritative server dispensing contents only if the contents fail to be found by searching an unstructured p2p system. We study the case when some peers may not be fully cooperative in the search process and examine the impact of various noncooperative behaviors in the aspect of scalability, more specifically average server load and average peer load as the peer population size increases. We categorize selfish peers into three classes: impatient peers that directly query the server without searching the p2p system, non-forwarders that refuse to forward query requests, and non-resolvers that refuse to share contents. It is shown that in the hybrid p2p system, impatient and/or non-forwarding behaviors prevent the system from scaling well because of the high server load, while the system scales well under the non-resolving selfish peers. Our study implies that the hybrid p2p system does not mandate an incentive mechanism for content sharing, which is in stark contrast to unstructured p2p systems, where incentivizing peers to share contents is known to be a key factor for the system's scalability.

Verifiable Auditing for Outsourced Database in Cloud Computing

The notion of database outsourcing enables the data owner to delegate the database management to a cloud service provider (CSP) that provides various database services to different users. Recently, plenty of research work has been done on the primitive of outsourced database. However, it seems that no existing solutions can perfectly support the properties of both correctness and completeness for the query results, especially in the case when the dishonest CSP intentionally returns an empty set for the query request of the user. In this paper, we propose a new verifiable auditing scheme for outsourced database, which can simultaneously achieve the correctness and completeness of search results even if the dishonest CSP purposely returns an empty set. Furthermore, we can prove that our construction can achieve the desired security properties even in the encrypted outsourced database. Besides, the proposed scheme can be extended to support the dynamic database setting by incorporating the notion of verifiable database with updates.

데이터 액세스 확률의 제곱근 법칙을 이용한 상호 관련 데이터 할당 기법

A data allocation technique is essential to improve the performance of data broadcast systems. This paper explores the issues for allocating data items on broadcast channels to process multiple-data queries in the environment where query profiles and query request rates are given, and proposes a new data allocation scheme named IDAS. The proposed scheme employs the strategy that the broadcast frequency of each data is determined by the square root value of its relative access probability. IDAS could enhance the performance of query response time since it can process queries of high request rate fast and show a resonable degree of query data adjacency. Simulation is performed to evaluate the performance of the proposed scheme. The simulation results show that IDAS outperforms other schemes in terms of the average response time.

Implementation of Privacy-Enhanced SMS Provider on the Android Platform

The Android platform stores basic telephony data such as contacts, call logs, schedules, and SMS messages. These basic telephony data are managed by ContentProvider, which is one of the core components of Android applications along with Activities, Services, and BroadcastReceivers. If an Android application requires basic telephony data, it requests queries such as query, insert, update, and delete operations to ContentProvider. In the Android platform, every operation for which there is a possibility of misuse is protected by permissions. Generally, every application with proper permissions can request a protected operation from the Android platform. Database operations which access a database through ContentProvider are also protected by READ and WRITE permissions. However, this security policy has a critical flaw: it is impossible to differentiate the permissions of individual contacts in the Android Platform. If one application has READ permission for contacts, it can read every contact stored on an Android device. When the entities are not equal value, this flaw becomes a critical flaw. In the particular case of SMS, the problem is more serious because SMS messages can include financial information, authentication tokens, or privacy information. To address this security problem, we have designed and implemented a privacy-enhanced SMS provider. In this paper, we show how to hide sensitive SMS data from untrusted applications.

FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments

Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Existing approaches to range-aggregate queries are insufficient to quickly provide accurate results in big data environments. In this paper, we propose FastRAQ-a fast approach to range-aggregate queries in big data environments. FastRAQ first divides big data into independent partitions with a balanced partitioning algorithm, and then generates a local estimation sketch for each partition. When a range-aggregate query request arrives, FastRAQ obtains the result directly by summarizing local estimates from all partitions. FastRAQ has O(1) time complexity for data updates and O(N/P×B) time complexity for range-aggregate queries, where N is the number of distinct tuples for all dimensions, P is the partition number, and B is the bucket number in the histogram. We implement the FastRAQ approach on the Linux platform, and evaluate its performance with about 10 billions data records. Experimental results demonstrate that FastRAQ provides range-aggregate query results within a time period two orders of magnitude lower than that of Hive, while the relative error is less than 3 percent within the given confidence interval.

Risk Aware Query Replacement Approach for Secure Databases Performance Management

Large amount of data and increased demand to extract, analyze and derive knowledge from data are impairing nowadays performance of enterprise mission-critical systems such as databases. For databases, the challenging problem is to manage complex and sometimes non-optimized queries executed on enormous data sets stored across several tables. This generally results in increased query response time and loss of employees productivity. In this paper, we investigate the problem of enterprise computing resources availability. Our goal is to minimize performance degradation arising from resource intensive queries. We propose a risk aware approach that decouples the process of analyzing resource requirements of sql queries from their execution. We leverage XACML to control users' requests and to monitor database loads. This allows us to adjust available resources in a database system to computing resource needs of queries. A query can therefore run in a database if it does not severely impact the performance of the database. Otherwise, we propose to the requester a replacement query denoted what-if-query. Such query proposes results that are similar to the results of the requester's query, is secure and provides acceptable answers when it executes without compromising the performance of the database.

Environment Based Secure Transfer of Data In Wireless Sensor Networks

Most critical sensor readings (Top-k Monitoring) in environment monitoring system are important to many wireless sensor applications. In such applications, sensor nodes transmit the data continuously for a specific time period to the storage nodes. It is responsible for transferring the received results to the Authority on Top-k Query request from them. Dummy data's were added into the original text data to secure the data against adversary in case of hacking the sensor and storage nodes. If storage node gets hacked by adversary, false details will be sent to the authority. An effective technique named aggregate signature to validate the source of the message and also to protect the data against latest security attacks, cryptography technique combined with steganography has been introduced. Indexed based scheme for the database access has also been proposed, to validate the resources against availability before forwarding the data fetch request to storage nodes from Authority.

Improved Research on Fuzzy Search over Encrypted Cloud Data Based on Keywords

A search strategy over encrypted cloud data based on keywords has been improved and has presented a method using different strategies on the client and the server to improve the search efficiency in this paper. The client uses the Chinese and English to achieve the synonym construction of the keywords, the establishment of the fuzzy-syllable words and synonyms set of keywords and the implementation of fuzzy search strategy over the encryption of cloud data based on keywords. The server side through the analysis of the user’s query request provides keywords for users to choose and topic words and secondary words are picked out. System will match topic words with historical inquiry in time order, and then the new query result of the request is directly gained. The analysis of the simulation experiment shows that the fuzzy search strategy can make better use of historical results on the basis of privacy protection for the realization of efficient data search, saving the search time and improving the efficiency of search.

Efficient traffic load reduction algorithms for mitigating query hotspots for wireless sensor networks

In query–based wireless sensor networks (WSNs), a hotspot may cause the heavy power consumption due to the frequent query request among nodes for specific events. A hotspot leads to the fluctuation of traffic flow and the unbalanced energy among those neighbouring nodes. To mitigate the hotspot phenomenon, a target region with the fluctuation of traffic flow around the hotspot to handle the unbalance traffic load is constructed first. In this paper, we propose two algorithms for mitigating query hotspots, distance–based mobile agent selection (DMAS) and greedy–based mobile agent selection (GMAS). The boundary node equipped with mobile agent is called limb node that moves around the hotspot. Experiment results include the comparisons of hotspot rate and total energy consumption, hotspot traffic rate with different RT value query frequency, and the effect of the hotspot overlay avoidance. The results show that DMAS and GMAS can mitigate the traffic rate of hotspot about 50%, effectively.

An Efficient Top-kQuery Processing with Result Integrity Verification in Two-Tiered Wireless Sensor Networks

In two-tiered wireless sensor networks, storage nodes take charge of both storing the sensing data items and processing the query request issued by the base station. Due to their important role, storage nodes are more attractive to adversaries in a hostile environment. Once a storage node is compromised, attackers may falsify or abandon the data when answering the query issued by the base station, which will make the base station get incorrect or incomplete result. This paper proposes an efficient top-kquery processing scheme with result integrity verification named as ETQ-RIV in two-tiered sensor networks. According to the basic idea that sensor nodes submit some encoded message containing the sequence relationship as proof information for verification along with their collected sensing data items, a data binding and collecting protocol and a verifiable query response protocol are proposed and described in detail. Detailed quantitative analysis and evaluation experiments show that ETQ-RIV performs better than the existing work in both communication cost and query result redundancy rate.

Using provenance to efficiently improve metadata searching performance in storage systems

In cloud storage systems, more than 50% of requests are metadata operations, and thus the file system metadata search performance has become increasingly important to different users. With the rapid growth of storage system scales in volume, traditional full-size index trees cannot offer high-performance metadata search due to hierarchical indexing bottleneck. In order to alleviate the long latency and improve the quality-of-service (QoS) in cloud storage service, we proposed a novel provenance based metadata-search system, called PROMES. The metadata search in PROMES is split into three phases: (i) leveraging correlation-aware metadata index tree to identify several files as seeds, most of which can satisfy the query requests, (ii) using the seeds to find the remaining query results via relationship graph search, (iii) continuing to refine and rerank the whole search results, and sending the final results to users. PROMES has the salient features of high query accuracy and low latency, due to files’ tight and lightweight indexing in relationship graph coming from provenance’s analysis. Compared with state-of-the-art metadata searching schemes, PROMES demonstrates its efficiency and efficacy in terms of query accuracy and response latency.

Bitmap lattice index in road networks

A novel technique called the bitmap lattice index (BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a server’s workload increases as the query request increases when a server sends a client information. To solve this problem, we propose the BLI. The BLI denotes an object and a node as 0 and 1 in the Hilbert curve (HC) map. The BLI can identify the position of a node and an object through bit information; it can also reduce the broadcasting frequency of a server by reducing the size of the index, thereby decreasing the access latency and query processing times. Moreover, the BLI is highly effective for data filtering, as it can identify the positions of both an object and a node. In a road network, if filtering is done via the Euclidean distance, it may result in an error. To prevent this, we add another validation procedure. The experiment is conducted by applying the BLI to kNN query, and the technique is assessed by a performance evaluation experiment.

The Music Retrieval System Based on the Frequently-Used Rules of Chinese Text

The conventional music retrieval system is mostly based on accurate matching of keywords, and it is difficult to satisfy the demand of intelligent retrieval. This paper presents a system of music retrieval based on the frequently-used rules of Chinese text. The system not only retains the conventional retrieval, but also is added the frequently-used rules of Chinese text that is “the attribute of the entity” (such as “the singer of the song”) into it. Firstly, the system segments the words of user’s query requests and checks that whether the user’s inputs comply with the rule of “the attribute of the entity”, and then it decides to adopt what kind of retrieval that is retrieval of text rules or conventional retrieval. The experiment proves that the system is better to satisfy the demand of user’s query requests than conventional music retrieval system while coping with the text rule of “the attribute of the entity”.

Secure semantic expansion based search over encrypted cloud data supporting similarity ranking

AbstractWith the advent of cloud computing, more and more information data are outsourced to the public cloud for economic savings and ease of access. However, the privacy information has to be encrypted to guarantee the security. To implement efficient data utilization, search over encrypted cloud data has been a great challenge. The existing solutions depended entirely on the submitted query keyword and didn’t consider the semantics of keyword. Thus the search schemes are not intelligent and also omit some semantically related documents. In view of the deficiency, as an attempt, we propose a semantic expansion based similar search solution over encrypted cloud data. Our solution could return not only the exactly matched files, but also the files including the terms semantically related to the query keyword. In the proposed scheme, a corresponding file metadata is constructed for each file. Then both the encrypted metadata set and file collection are uploaded to the cloud server. With the metadata set, the cloud server builds the inverted index and constructs semantic relationship library (SRL) for the keywords set. After receiving a query request, the cloud server first finds out the keywords that are semantically related to the query keyword according to SRL. Then both the query keyword and the extensional words are used to retrieve the files. The result files are returned in order according to the total relevance score. Eventually, detailed security analysis shows that our solution is privacy-preserving and secure under the previous searchable symmetric encryption (SSE) security definition. Experimental evaluation demonstrates the efficiency and effectives of the scheme.