Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Since customers typically tend to buy sets of items (i.e., itemsets) together, several research efforts have been undertaken towards facilitating itemset placement in retail stores for improving retailer revenue. However, they fail to consider that the time-period of expiry can vary across items i.e., some items expire sooner than others. This leads to loss of opportunity towards improving retailer revenue. Hence, we propose PEAR, which is a Product Expiry-Aware and Revenue-conscious itemset placement scheme for improving retailer revenue. Our key contributions are three-fold. First, we introduce the problem of addressing retail itemset placement when the items can be associated with different time-periods of expiry. Second, we propose the expiry-aware PEAR scheme for efficiently identifying and placing high-revenue itemsets for improving retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate that PEAR is indeed effective in improving retailer revenue w.r.t. a reference scheme.

Abstract Next-generation enterprise management systems are beginning to be developed based on the Systems of Engagement (SOE) model. We visualize an SOE as a set of entities. Each entity is modeled by a single parent document with dynamic embedded links (i.e., child documents) that contain multi-modal information about the entity from various networks. Since entities in an SOE are generally queried using keywords, our goal is to efficiently retrieve the top-k entities related to a given keyword-based query by considering the relevance scores of both their parent and child documents. Furthermore, we extend the afore-mentioned problem to incorporate the case where the entities are geo-tagged. The main contributions of this work are three-fold. First, it proposes an efficient bitmap-based approach for quickly identifying the candidate set of entities, whose parent documents contain all queried keywords. A variant of this approach is also proposed to reduce memory consumption by exploiting skews in keyword popularity. Second, it proposes the two-tier HI-tree index, which uses both hashing and inverted indexes, for efficient document relevance score lookups. Third, it proposes an R-tree-based approach to extend the afore-mentioned approaches for the case where the entities are geo-tagged. Fourth, it performs comprehensive experiments with both real and synthetic datasets to demonstrate that our proposed schemes are indeed effective in providing good top-k result recall performance within acceptable query response times.

Product placement in retail has a significant impact on the sales revenue of retailers. Hence, research efforts are being made to improve retailer revenue using high-utility pattern mining based product placement approaches. However, none of these existing approaches has explored generalized high-utility itemset mining for determining product placement in retail. The knowledge of generalized high-utility itemsets extracted from user purchase transactional database in conjunction with a product taxonomy can provide new insights about customer purchase behaviour. This work proposes the generalized utility itemset (GUI) index for retrieving generalized high-utility (revenue) itemsets. We also present a framework, which leverages the GUI index towards retail product placement to improve revenue. Our performance study using real datasets shows the effectiveness of our proposed scheme w.r.t. two existing schemes.

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. Typically, pattern mining is carried out from huge databases, which tend to get updated several times. Consequently, as a given database is updated, some of the patterns discovered may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of Incremental Mining. The goal of incremental mining is to efficiently and incrementally mine patterns when a database is updated as opposed to mining all of the patterns from scratch from the complete database. Incidentally, research efforts are being made to develop incremental pattern mining algorithms for extracting different kinds of patterns such as frequent patterns, sequential patterns and utility patterns. However, none of the existing works addresses incremental mining in the context of coverage patterns, which has important applications in areas such as banner advertising, search engine advertising and graph mining. In this regard, the main contributions of this work are three-fold. First, we introduce the problem of incremental mining in the context of coverage patterns. Second, we propose the IncCMine algorithm for efficiently extracting the knowledge of coverage patterns when incremental database is added to the existing database. Third, we performed extensive experiments using two real-world click stream datasets and one synthetic dataset. The results of our performance evaluation demonstrate that our proposed IncCMine algorithm indeed improves the performance significantly w.r.t. the existing CMine algorithm.

Group buying offers products at significantly reduced prices on the condition that a pre-specified number of buyers would make the purchase. Given the ever-increasing popularity of mobile devices and applications coupled with the typically high price-sensitivity of a significant percentage of users, group buying in mobile environments has the potential to attain dramatically increasing popularity. However, existing solutions for group buying typically involve web-based portals, which are not capable of handling user mobility. Hence, this work proposes an end-to-end mobile group-buying system that can be used for targeted decentralized advertisement and discovery of group buying deals, and group formation to avail a deal. The key contributions are three-fold. First, it proposes an ILP (Integer Linear Programming)-based optimal algorithm for the problem of efficiently forming groups of buyers with the objective of maximizing the overall utility of the solution. Second, it proposes a greedy algorithm for the same problem since solving ILP can take significant time for some problem instances. The greedy algorithm takes an input parameter, which can be tweaked to trade-off its optimality with its running time. Third, performance study shows that the proposed algorithms exhibit good performance in terms of the number of groups formed w.r.t. The requests in the system. Notably, the greedy algorithm provides near-optimal solution and runs significantly faster than the ILP-based optimal algorithm.

The prevalence of mobile devices and applications strongly motivate mobile crowdsourcing for facilitating location-dependent services. We propose LoRUS, a Location-based Relevant User determination System for efficiently retrieving the top-k relevant mobile users in a given spatial window.

Information about resident involvement in reporting various city-related events (e.g., Potholes, traffic jams) via mobile apps is critical to key stakeholders for effective city management. While existing efforts have primarily focused on event data collection in space, they are not capable of performing efficient retrieval of information about resident involvement in event reporting across different spatial regions and at different spatial granularities. Hence, this work makes the following contributions. First, we present CRIS, a scalable system for efficient retrieval of resident involvement and event information across different spatial regions and at varying spatial granularities. Second, we propose the euR-tree, a novel R-tree-based index augmented with (a) a hash-based array for indexing events in space, and (b) fixed-length arrays for indexing resident involvement information in reporting events in space. The euR-tree is integrated into CRIS to realize efficient retrieval. Third, our performance study indicates that the euR-tree is indeed effective in performing such retrieval with reduced query response times and disk I/Os.

This is a tutorial paper covering issues associated dynamic management of information as well as content in wireless networks of different types such as Mobile Peer-to-Peer (MP2P), Vehicle-to-Vehicle (V2V) and Delay-Tolerant Networks (DTNs).

Awareness of dynamically changing road conditions is crucial for a safe and quality driving experience, as well as, in augmenting trip planning. This work addresses the problem of keeping users informed in a timely and personalized manner about road conditions arising from both scheduled and ad hoc events. We propose Road Eye, a system for personalized retrieval of dynamic road conditions. The key contribution of Road Eye is the psi R-tree, which is a novel R-tree-based index augmented with linked lists for facilitating quick and personalized retrieval of user-queried road conditions. Our performance study indicates that the psi R-tree is indeed effective in retrieving dynamic road conditions with reduced query response times and disk I/Os.

Cities typically face a wide gamut of management and maintenance problems. Existing automated sensor based solutions are prohibitively expensive to deploy. Furthermore, these solutions need to be complemented by incorporating human judgment for accurate, timely and cost-effective city-related event identification. To this end, this work proposes City Zen, which is a novel platform for event reporting and analytics to engage residents towards city management through incentives. Key contributions include: (a) the City Zen platform with an app for enabling authenticated residents to report events in a city for end-to-end integrated smart city management, (b) Differentiated incentive management based on types and priorities of events, quality and timeliness of event reports as well as resident intent, and (c) a social dashboard for searching events and subscribing for event alerts with additional ability to provide feedback on the event reports. Ongoing pilots and our performance study indicate that the platform indeed performs city management cost-effectively depending on the incentive mechanisms used for engaging residents.

We propose the E-VeT system for efficient vehicular traffic management in road networks using economy-based reward/penalty schemes. In E-VeT, base stations collaboratively facilitate dynamic vehicular route assignments for reducing the traffic congestion, average time of arrival and fuel consumption. The main contributions of this work are two-fold. First, it proposes an R2A (Revenue-based Route Allocation) scheme, which rewards vehicles for following system-assigned longer-time paths, and charges a fee for following system-assigned shorter-time paths. Furthermore, it penalizes vehicles for any deviations from the system-assigned paths. Second, it discusses a route allocation algorithm, which gives lesser-time paths as a preference to vehicles that have earned higher revenue based on the R2A scheme. Preliminary performance study shows that E-VeT is indeed effective in managing vehicular traffic in road networks by reducing the average time of arrival and fuel consumption.

For realizing such crowdsourcing-related M-P2P applications, dynamic data management becomes a necessity to improve data availability, given the mobility and fragile wireless connections that connect resource-constrained mobile devices. Moreover, unlike in the case of traditional environments such as clusters, free-riding is a major issue for M-P2P environments, thereby implying that economic models may play a better role in incentivizing peers to collaborate for data discovery and management. Furthermore, traditional methods of data management in mobile environments generally consider only single-hop client-server communication. On the other hand,in M-P2P networks, the network communication is multihop and mobile devices can collect real-time data. Finally, privacy issues need to be addressed effectively to prevent location-based service providers from misusing users' location information.

This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords. Given a set of keywords from a document, mCK query can be very useful in geotagging the document by comparing the keywords to other geotagged documents in a database. To answer mCK queries efficiently, we introduce a new index called the bR*-tree, which is an extension of the R*-tree. Based on bR*-tree, we exploit a priori-based search strategies to effectively reduce the search space. We also propose two monotone constraints, namely the distance mutex and keyword mutex, as our a priori properties to facilitate effective pruning. Our performance study demonstrates that our search strategy is indeed efficient in reducing query response time and demonstrates remarkable scalability in terms of the number of query keywords which is essential for our main application of searching by document.

Data management in mobile peer to peer (M-P2P) systems needs dynamic data management due to mobility and fragile wireless connection connecting resource constraint devices. Traditional methods of data management and services in mobile P2P environment generally assume all peers to cooperate. Since peer activities in M-P2P are not generally monitored, users assume that they are free to use the resources anyway they like. Under this feeling of freedom, a subset of users (free riders) begins to consume much more resources available on M-P2P than they wish to contribute. In addition, due to the dynamic nature of moving hosts, topology changes very often and traditional schemes fall short in providing reasonable data availability. This becomes much more important in M-P2P where the network communication is generally multi-hop and intermediate peers have to render relay services other than data providers to improve the connectivity. Economic-based incentive schemes have been proposed which may play a better role in inciting free riders to collaborate. The data and service availability can be increased by associating a price with data items and services. In such schemes, peers can bid for better services, intermediate peers can earn incentives by providing relay services and in fact, outgoing peers can lease data items to others to still earn incentives while disconnected. New peers can become data providers by providing hosting services to earn incentives. This tutorial will explore issues involved in managing resources using economic incentives.

In mobile ad-hoc peer-to-peer (M-P2P) networks, economic models become a necessity for enticing non-cooperative mobile peers to provide service. M-P2P users may issue queries with varying constraints on query response time, data quality of results and trustworthiness of the data source. This work proposes ConQuer, which addresses constraint queries in economy- based M-P2P networks. ConQuer proposes a broker-based incentive M-P2P model for handling user-defined constraint queries. It also provides incentives for MPs to form collaborative peer groups for maximizing data availability and revenues by mutually allocating and deallocating data items using a royalty-based revenue-sharing method. Such reallocations facilitate MPs in providing better data quality, thereby allowing them to further increase their revenues.

This paper proposes CADRE (collaborative allocation and deallocation of replicas with efficiency), a dynamic replication scheme for improving the typically low data availability in mobile ad-hoc peer-to-peer (M-P2P) networks. The main contributions of CADRE are two-fold. First, it collaboratively performs both replica allocation and deallocation in tandem to facilitate optimal replication and to avoid 'thrashing' conditions. Second, it addresses fair replica allocation across the MHs. CADRE deploys a hybrid super-peer architecture in which some of the MHs act as the 'gateway nodes' (GNs) in a given region. GNs facilitate both search and replication. Our performance study indicates that CADRE indeed improves query response times and data availability in M-P2P networks as compared to some recent existing schemes

The ever-increasing popularity of mobile applications coupled with the prevalence of spatial data has created the need for efficient processing of spatial queries in mobile environments. While different types of spatial queries (e.g., spatial select queries, spatial join queries and nearest neighbour queries) need to be addressed in mobile environments, this work specifically addresses the processing of spatial select queries (i.e., window queries) on any k relations among N spatial relations. We designate such window queries on any k relations among N spatial relations as kNW queries. Notably, the processing of kNW queries is much more challenging in mobile environments than in traditional environments primarily due to the mobility of the clients which issue the queries to the respective base stations. The main contribution of this work is the proposal of the kNR-tree, a single integrated novel R-tree-based structure for indexing objects from N different spatial relations. Notably, the kNR-tree facilitates efficient processing of kNW queries. Our performance evaluation demonstrates that our proposed technique, which is based on the kNR-tree, is indeed effective in reducing the response times of kNW queries in mobile environments.

Parallel database systems are increasingly being deployed to support the performance demands of end-users. While declustering data across multiple nodes facilitates parallelism, initial data placement may not be optimal due to skewed workloads and changing access patterns. To prevent performance degradation, the placement of data must be reorganized, and this must be done on-line to minimize disruption to the system.