Abstract

Symbolic sequence data is generated from applications such
as human activity recognition, financial modeling, intrusion detection
systems, and drug discovery tasks. Extracting frequent patterns from
symbolic data is an important research problem. In the literature, sev-
eral CPU-based approaches have been proposed for pattern extraction
from symbolic sequence data. However, due to their sequential execution,
CPU-based approaches suffer from performance issues in extracting pat-
terns from large sequence data. In modern computing infrastructures,
GPUs are playing a central role in carrying out high-performance com-
puting tasks due to their parallel design. Consequently, researchers are
investigating GPU-based data mining techniques for tasks such as pat-
tern mining, clustering, and classification. In this paper, we propose a
two-level indexing scheme that utilizes extra auxiliary memory to fa-
cilitate the efficient extraction of frequent patterns from symbolic data
by exploiting the parallel processing capabilities of GPUs. Experimental
results on three real-world datasets demonstrate that the proposed ap-
proach achieves substantial runtime improvements--approximately 4×
speedup over traditional CPU-based algorithms for smaller support val-
ues (e.g., 5), and more than 20× speedup for support thresholds greater
than 10--when applied to large symbolic sequence data, outperforming
both CPU-based and naïve GPU-based methods

Abstract

Display advertising is one of the predominant modes of online advertising. Given a set of available ad slots on the web pages, a publisher makes efforts to allocate them to advertisers, satisfying their demand and maximizing their revenue. Investigating efficient approaches for ad slot allocation to advertisers is a research issue. Given a set of ad slots from different pages, advertisers aim to deliver the ad to a specified number of targeted users. Exposing a user repeatedly to the same ad may result in a potential loss of revenue for the publisher and create boredom for the user. There is an opportunity to improve the publisher's revenue by ensuring that each ad slot allocated to an advertiser reaches a distinct number of users. In the literature, efforts are being made to address this problem by modeling it as an optimization problem. These approaches suffer from the issues of complexity and practicality. We approach the problem with the pattern-mining framework by employing the notions of "coverage" and "overlap". In the proposed approach, we exploit the knowledge of coverage patterns extracted from click stream transactions and propose a comprehensive, efficient, and practical ad slot allocation framework. The experimental results on two real-world click stream datasets show that the proposed approach could improve the revenue of the publisher by meeting the demands of the increased number of advertisers by reducing the repeated display of advertisements to users.

Abstract

Data science and artificial intelligence (DSAI) based methods can process massive amounts of data to extract useful knowledge and can be employed to build decision support systems in various domains. Like other domains, the legal systems in several countries are being digitized and there is a scope to build DSAI-based frameworks to improve the performance of legal systems. In the literature, research efforts are being made to investigate DSAI-based methods to improve justice delivery. The Indian legal system is currently experiencing a major problem with a substantial backlog of cases. This paper provides an overview of DSAI-based efforts in the legal domain related to India. We also listed potential research issues to be explored. We hope the issues will encourage further research to improve justice delivery performance in India and other countries.

Abstract

A graph transactional database (GTD) is a collection of graphs. Frequent Top-k Subgraph Pattern Mining (FTSPM) involves finding the complete set of top-k frequently occurring subgraph patterns in a GTD. Most previous studies focused on finding these patterns in certain graphs by disregarding the crucial information regarding the existential probabilities that may exist between the edges of any two nodes. With this motivation, this paper proposes a novel model to discover top-k (frequently occurring) subgraphs in an uncertain graph transactional database. We introduce a novel algorithm, top-k uncertain subgraph miner (TUSM), to find all top-k subgraphs in the data. We also introduce an approximate top-k uncertain subgraph miner (ATUSM) algorithm to tackle the computational expansiveness of TUSM. Experimental results on synthetic and protein-protein interaction datasets demonstrate that the proposed model finds valuable information and the algorithms are efficient.

Abstract

Location-based services (LBSs) in a mobile network environment that provide personalized and timely information to users entail privacy concerns due to the leakage of user locations to adversaries. In the literature, several cloaking-based privacy preservation approaches have been proposed by considering the P2P environment. However, existing spatial cloaking approaches form a large cluster of mobile users to cloak the user query location. Maintaining such structures in a highly dynamic mobile-P2P network is challenging. In addition to user's location, the user's intent is also an important concern and needs to be preserved from an adversary. This paper proposes a location privacy and intent privacy preservation scheme, especially for spatial range queries. The key contributions of our work are three-fold. First, we introduce the concept of ijk-anonymity to achieve improved location privacy. Second, we propose a location-based privacy-preservation approach, which we designate as ijkCloak, for spatial range queries in a mobile network environment. The proposed approach preserves user location and intent information from the LBS provider and nearby peers. Third, we conduct theoretical analysis and experiments on resistance to attacks. We show that ijkCloak effectively facilitates improved user location privacy and intent privacy by employing fewer peers w.r.t. existing approaches.

Abstract

Over a decade, efforts have been made to extend data science-based approaches to improve the process of drug-target interactions (DTIs) by modeling drugs and targets as graphs. From a drug discovery point of view, a pharmacophore, a subgraph of a drug candidate, is an essential structural and chemical feature necessary for interacting with a specific biological target. Effective identification of potential pharmacophores from a given candidate drug is a research issue. In the literature, efforts are being made to investigate machine learning and Graph Neural Network (GNN) based frameworks to identify the potential drug candidates for a given target. However, the problem of extracting the knowledge of the potential pharmacophore of the given candidate drug has not been explored. There is an opportunity to extract pharmacophores from drug candidates by exploiting the knowledge of potential subgraphs extracted through a trained GNN model. In this paper, we propose a two-phase GNN-based framework for identifying drug candidates and extracting potential pharmacophores from these candidates. The framework consists of two parts. First, we employ a GNN-based model to compute the affinity score for the given drug compound. Second, by employing the Monte Carlo Tree Search (MCTS) algorithm, the trained GNN is leveraged to extract potential subgraphs representing pharmacophores. The experimental results on the Davis, Kiba and Allergy datasets demonstrate the feasibility of the proposed approach to extract pharmacophores of candidate drugs with high performance. The predicted binding affinities of molecular subgraphs extracted from drug candidates are very similar to those of the corresponding drug candidates. The proposed approach helps explore the potential pharmacophore of the given candidate drug, which enhances the explainability of DTI to obtain deeper insights into crucial molecular interactions.

Abstract

In decision making settings such as medical diagnosis, investment choices, or sentencing in a court of law, an individual's background and experience influence their decisions. In the legal domain, multiple factors like personal bias/beliefs, recent events, and the contemporary state of mind could affect decision-making, leading to inconsistencies in judgments between and within jurisdictions. It is widely reported in the literature that anomalies and disparities exist in judicial decisions, such as bail grants and sentence impositions, stemming from implicit bias or
other contextual factors. Notably, in domains like sales and marketing, data cube-based systems are being used to extract interesting trends and
anomalies in subspaces from large multidimensional databases. In this paper, we extend the data cube framework to explore possible anomalies
and disparities in court sentences by conducting experiments on a sample Indian judgment dataset of criminal cases. The results show that the
data cube-based framework could identify anomalies in the legal domain. We hope this work will encourage researchers to investigate a compre-
hensive data cube-based framework to reduce disparities and improve
justice delivery worldwide.

Abstract

In the current digital era, about 80% of the digital data which is being generated is unstructured and unlabeled natural language text. In the development cycle of information retrieval and text mining applications, text representation is the most fundamental and critical step, as its effectiveness directly impacts the application's performance. The existing traditional text representation frameworks are mostly frequency distribution-based. In this work, we explored the spatial distribution of word embeddings and proposed two text representational models. The experimental demonstrated that proposed models perform consistently better at text mining tasks compared to baseline methods.

Abstract

Given a retail transactional database, the objective of high-utility pattern mining is to discover high-utility itemsets (HUIs), i.e., itemsets that satisfy a user-specified utility threshold. In retail applications, when purchasing a set of items (i.e., itemsets), consumers seek to replace or substitute items with each other to suit their individual preferences (e.g., Coke with Pepsi, tea with coffee). In practice, retailers, too, require substitutes to address operational issues like stockouts, expiration, and other supply chain constraints. The implication is that items that are interchangeably purchased, i.e., substitute goods, are critical to ensuring both user satisfaction and sustained retailer profits. In this regard, this work presents (i) an efficient model to identify HUIs containing substitute goods in place of items that require substitution, (ii) the SubstiTution-based Itemset indeX (STIX) to retrieve HUIs containing substitutes, and (iii) an experimental study to depict the benefits of the proposed approach w.r.t. a baseline method.

Abstract

Editorial note