Database Systems for Advanced Applications

The popularity of P2P (Peer-To-Peer) systems is increased tremendously due to massive increase in the Internet based applications. Initially, P2P systems were mainly designed for wired networks but today people are using more wireless networks and therefore these systems are gaining popularity. There are many wireless networks available today and WMNs (Wireless Mess Networks) are gaining popularity due to hybrid structure. People are using structured P2P systems-based applications within perimeter of a WMN. Structured P2P WMNs will assist the community to fetch the relevant information to accomplish their activities. There are inherent challenges in the structured P2P network and increased in wireless environment like WMNs. Structured P2P systems suffer from many challenges like lack of content availability, malicious content distribution, poor search scalability, free riding behaviour, white washing, lack of a robust trust model etc. Whereas, WMNs have limitations like mobility management, bandwidth constraint, limited battery power of user's devices, security, maintenance etc. in remote/ forward areas. We exploit the better possibility of content availability and search scalability in this paper. We propose replication schemes based on the popularity of content for structured P2P system applications in community based WMNs. The analysis of the performance shows that proposed scheme performs better than the existing replication scheme in different conditions.

P2P networks are important parts of the next generation Internet and P2P file sharing has become one of the most important internet application systems in the world. But P2P network faces a challenge in locating resource: the unstructured P2P network has complex search functions, but the efficiency of resource locating is low. However, it is efficient to locate resources in structure P2P network, but it does not support fuzzy queries and has the higher maintenance cost of network. So how to search the resources in the P2P networks efficiently is becoming the key problem. In traditional network structure, it is difficult to simultaneously meet users' requirements in resource locating efficiency and recall ratio. So this paper proposes a sub-domain hybrid P2P network model (SHPNM), which makes full use of the advantages of supporting fuzzy queries in the unstructured network and maintains efficient in resource locating in structured P2P network. Then it gives a detailed analysis about the design idea of SHPNM as well as the method of resources locating in this model. The nodes are gathered into a domain according to the approaching of nodes' physical location, and the nodes in a domain can form a structured P2P network. Each domain provides two nodes with biggest comprehensive performance value as a boundary node and a backup node, and the boundary nodes are connected together in the form of unstructured P2P networks. Simulations results show that SHPNM can both improve efficiency in locating resource and promote the recall ratio.

Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.

With the onset of the digital era and the availability of the internet, the need for digital data in a huge manner can be fulfilled by peer-to-peer (P2P) network instead of a traditional client server-based solution. Generally, mobile communication is based on cellular networks or multi-hop wireless networks. Cellular networks have adequate fixed infrastructure whereas multi-hop wireless networks have limited infrastructure and hence there are many limitations. The P2P systems are mainly designed for wired networks and the routing is based on IP infrastructure. Chord based protocols are widely deployed in the structured P2P networks but it can not perform well when implemented for mobile P2P networks due to the mobility of the users. Mobility pattern of mobile users plays an important role in locating users and delivering data packets seamlessly. Today, many of the mobile users follow a fixed mobility pattern in urban cities and mobility pattern of the mobile users can be utilized to reduce table update cost and increase Lookup Success Rate (LSR). We have proposed Local P2P Group (LPG) based communication scheme for structured mobile P2P networks. We are focussed on the mobility pattern of the mobile users in urban cities. We have analytically evaluated the proposed scheme using fluid-flow and RWP (Random Waypoint) mobility models and found that the proposed scheme performs better than the existing schemes like MR-Chord and MobiStore. Our proposed scheme has up to 40% higher Lookup Success Rate and 81% less table update cost than existing schemes, MR-Chord and MobiStore.

This paper looks at the processing of skyline queries on peer-to-peer (P2P) networks. We propose Skyframe, a framework for efficient skyline query processing in P2P systems, which addresses the challenges of quick response time, low network communication cost and query load balancing among peers. Skyframe consists of two querying methods: one is optimized for network communication while the other focuses on query response time. These methods are different in the way in which the query search space is defined. In particular, the first method uses a high dominating point that has a large dominating region to prune the search space to achieve a low cost in network communication. On the other hand, the second method relaxes the search space in order to allow parallel query processing to speed up query response. Skyframe achieves query load balancing by both query load conscious data space splitting/merging during the join/departure of nodes and dynamic load migration. We further show how to apply Skyframe to both the P2P systems supporting multi-dimensional indexing and the P2P systems supporting single-dimensional indexing. Finally, we have conducted extensive experiments on both real and synthetic data sets over two existing P2P systems: CAN (Ratnasamy in A scalable content-addressable network. In: Proceedings of SIGCOMM Conference, pp. 161---172, 2001) and BATON (Jagadish et al. in A balanced tree structure for peer-to-peer networks. In: Proceedings of VLDB Conference, pp. 661---672, 2005) to evaluate the effectiveness and scalability of Skyframe.

In many decision-making applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multi-dimensional dataset. In a high-dimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness of data points based on how often they are returned in the skyline when different number of dimensions (i.e., subspaces) are considered. Intuitively, a point with a high skyline frequency is more interesting as it can be dominated on fewer combinations of the dimensions. Thus, the problem becomes one of finding top-k frequent skyline points. But the algorithms thus far proposed for skyline computation typically do not scale well with dimensionality. Moreover, frequent skyline computation requires that skylines be computed for each of an exponential number of subsets of the dimensions. We present efficient approximate algorithms to address these twin difficulties. Our extensive performance study shows that our approximate algorithm can run fast and compute the correct result on large data sets in high-dimensional spaces.

Skyline query which relies on the notion of Pareto dominance filters the data items from a database by ensuring only those data items that are not worse than any others are selected as skylines. However, the dynamic nature of databases in which their states and/or structures change throughout their lifetime to incorporate the current and latest information of database applications, requires a new set of skylines to be derived. Blindly computing skylines on the new state/structure of a database is inefficient, as not all the data items are affected by the changes. Hence, this paper proposes a rule-based approach in tackling the above issue with the main aim at avoiding unnecessary skyline computations. Based on the type of operation that changes the state/structure of a database, i.e. insert/delete/update a data item(s) or add/remove a dimension(s), a set of rules are defined. Besides, the prominent dominance relationships when pairwise comparisons are performed are retained; which are then utilised in the process of computing a new set of skylines. Several analyses have been conducted to evaluate the performance and prove the efficiency of our proposed solution.

In P2P networks, it is effective to allocate replicas of each data item to multiple peers for improving search efficiency and data availability. It has been mathematically proved that the square-root allocation, in which the ratios of numbers of replicas are proportional to the square-root of their access frequencies, is optimal in terms of search efficiency. In this paper, we propose a replica relocation method that not only nearly achieves the square-root allocation but also distributes replicas uniformly in the network as much as possible. Our method creates replicas at each peer on the path along which a query is successfully forwarded. Here, each peer on the path determines whether it creates the replica or not based on the access frequency of the data item. In addition, for creating a new replica when a cache memory space is full, our method preferentially deletes a replica of a data item which has been replicated at a large number of peers.

Full-text keywords search of the Web over structured peer-to-peer networks shows promise to become an alternative to the state-of-the-art search engines since P2P overlays propose means for decentralized search across widely-distributed document collections. However, a disadvantage of structured P2P systems is that they consider only the problem of searching for keys, and thus cannot perform content-based retrieval. To deal with this problem, in this paper we consider a full-text retrieval problem in structured P2P networks. Our keyword searching engine BI-Chord is build on top of DHT-based P2P systems, entirely distributed, uses bloom filters and inverted index and therefore scales well with the size of the network. Experimental results show that our mechanism is efficient, scalable and provides high quality of search results, i.e. the precision and recall metrics.

Distributed Hash Tables and unstructured Peer-to-Peer networks present an alternative basis for global software applications. Compared to the Client-Server applications, P2P applications have significant advantages. By eliminating the idea of Servers as the single point of failures and single point of control out of the picture. P2P networks are suitable for many applications like Distributed Online Social Networks the modern society needs today. Distributed Applications built on top of a P2P network are much more resistant to complete system breakdowns, power misuse by authorities (e.g. censorship) and limitless surveillance. In previous work, we presented Distributed Data Structure (DDS) offers a middle-ware for distributed applications. This software has been implemented in a simulation framework for P2P networks called PeerFactSim.KOM. The DDS middle-ware works on top of a Distributed Hash Table (DHT) overlay as a structured P2P network and offers an object-oriented, distributed storage layer. The security of data is an essential topic in such environments. Therefore, without proper security mechanisms, the idea of storing private and sensitive data on unknown network peers become useless, as the stored data can be read or manipulated. In this paper, we present and evaluate a concept of a secure model working completely without trusted nodes for such distributed data structures in peer-to-peer networks. In the evaluation, we show that the time and storage overhead introduced through the security architecture for DDS comes with acceptable proportions for large P2P applications.

Skyline queries help users make intelligent decisions over complex data. The main shortcoming of skyline queries is that the cardinality of the result set is not known a-priori. To overcome this limitation, the representative skyline query has been proposed, which retrieves a fixed set of k skyline points that best describe all skyline points. Even though the representative skyline has been studied before in centralized environments, this is the first paper that addresses efficient computation of the representative skyline in distributed systems. The distributed nature of the environment makes the task of discovering truly representative skyline points even more challenging. In this paper, we propose a novel framework for discovering the representative skyline over distributed data sources. Our experimental study demonstrates the efficiency and effectiveness of our framework.KeywordsRepresentative PointSkyline QuerySkyline PointCommunication PhaseLocal SkylineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An increasing number of large-scale applications exploit peer-to-peer network architecture to provide highly scalable and flexible services. Among these applications, data management in peer-to-peer systems is one of the interesting domains. In this paper, we investigate the multidimensional skyline computation problem on a structured peer-to-peer network. In order to achieve low communication cost and quick response time, we utilize the iMinMax(\theta ) method to transform high-dimensional data to one-dimensional value and distribute the data in a structured peer-to-peer network called BATON. Thereafter, we propose a progressive algorithm with adaptive filter technique for efficient skyline computation in this environment. We further discuss some optimization techniques for the algorithm, and summarize the key principles of our algorithm into a query routing protocol with detailed analysis. Finally, we conduct an extensive experimental evaluation to demonstrate the efficiency of our approach.

Outsourcing encrypted time series data and query services to a cloud has been widely adopted by data owners for economic considerations. However, it inevitably lowers data utility and query efficiency. Existing secure skyline query schemes either leak critical information or are inefficient. In this paper, we propose an efficient and privacy-preserving interval skyline query scheme by employing symmetric homomorphic encryption (SHE). Specifically, we first devise a secure sort protocol to sort the encrypted dataset and a secure high-dimensional dominance check protocol to securely determine dominance relations of time series data, in which a dominance check tree is presented. With these secure protocols, we propose our secure skyline computation protocol that can ensure both security and efficiency. Furthermore, to deal with the characteristics of time series data, we design a look-up table to index time series for quick query response. The security analysis shows that our proposed scheme can protect outsourced data, query results, and single-dimensional privacy and hide access patterns. In addition, we evaluate our proposed scheme and compare the core component of our scheme with the state-of-the-art solution, and the results indicate that our protocol outperforms the compared solution by two orders of magnitude in the computational cost and at least 23× in the communication cost.

The application of skyline queries on outsourced databases significantly aids online analysis, yet efficiently handling encrypted queries remains a formidable obstacle. Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the <u>H</u>onest-<u>M</u>ajority and <u>M</u>aliciously <u>S</u>kyline <u>Q</u>uery scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. The core of HMMSQ is an optimized skyline diagram constructed by a novel skyline region-splitting algorithm for accurate skyline queries. Furthermore, it mitigates the frequency of dataset accesses by leveraging a multi-path R-tree for secure skyline retrieval. Notably, the majority of malicious behavior detection is focused on the servers, thereby minimizing user authentication overhead. The complexity and security are thoroughly analyzed, and experimental evaluations on various datasets demonstrate its efficiency and practicality in terms of computational cost and communication overhead. Remarkably, HMMSQ outperforms existing methods in query latency, achieving up to an order of magnitude improvement.

P2P network is a focus research field with rapid development in recent years. Distributed hash tables (DHT for short) schemes are the core technique to locate and search resources in structured P2P network. However, due to the characteristic of DHT that its hash method destroy data locality, DHT only support exact-match search, cannot support complex search process. A simple and flexible solution has been built to tackle the problem which is known as over-DHT paradigm without need to modify existing DHT infrastructures which significantly decreases the complexity of deployment. Among the well known over-DHT paradigms now, LIGHT is an elegant one. It provides more efficient complex query methods than other paradigms and also reduces the paradigm maintenance cost. However, LIGHT does not make full use of the advantages of space partition tree such as that it is a complete binary tree and divides data space into uniform interval in each level of the tree. In this paper, we propose an improved range query algorithm, Upper Boundary Predictably Lookup (UBPL for short) to reduce the DHT-lookup times during range query procedure in LIGHT. We also proved the effectiveness of UBPL by simulating experiments.