Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.

Governments are embracing an open data philosophy and making their data freely available to the public to encourage innovation and increase transparency. However, the number of available datasets is still limited. Finding relationships between related datasets on different data portals enables users to search the relevant datasets. These datasets are generated from the training data, which need to be curated by the user query. However, relevant dataset retrieval is an expensive operation due to the preparation procedure for each dataset. Moreover, it requires a significant amount of space and time. In this study, we propose a novel framework to identify the relationships between datasets using structural information and semantic information for finding similar datasets. We propose an algorithm to generate the Concept Matrix (CM) and the Dataset Matrix (DM) from the concepts and the datasets, which is then used to curate semantically related datasets in response to the users’ submitted queries. Moreover, we employ the proposed compression, indexing, and caching algorithms in our proposed scheme to reduce the required storage and time while searching the related ranked list of the datasets. Through extensive evaluation, we conclude that the proposed scheme outperforms the existing schemes.

Graph data are pervasive worldwide, e.g., social networks, citation networks, and web graphs. A real-world graph can be huge and requires heavy computational and storage resources for processing. Various graph compression techniques have been presented to accelerate the processing time and utilize memory efficiently. SOTA approaches decompose a graph into fixed-size submatrices and compress it by applying the existing graph compression algorithm. This approach is promising if the input graph is dense. Otherwise, an optimal graph compression ratio cannot be achieved. Graphs such as those used by social networks exhibit a power-law distribution. Thus, applying compression to the fixed-size block of a matrix could lead to the empty cell processing of that matrix. In this paper, we solve the problem of ordered matrix compression on a deep level, dividing the block into sub-blocks to achieve the best compression ratio. We observe that the ordered matrix compression ratio could be improved by adopting variable-shape regions, considering both horizontal- and vertical-shaped regions. In our empirical evaluation, the proposed approach achieved a 93.8% compression ratio on average, compared with existing SOTA graph compression techniques.

Keyphrase Prediction (KP) is essential for identifying keyphrases in a document that can summarize its content. However, recent Natural Language Processing (NLP) advances have developed more efficient KP models using deep learning techniques. The limitation of a comprehensive exploration jointly both keyphrase extraction and generation using pre-trained language models spotlights a critical gap in the literature, compelling our survey paper to bridge this deficiency and offer a unified and in-depth analysis to address limitations in previous surveys. This paper extensively examines the topic of pre-trained language models for keyphrase prediction (PLM-KP), which are trained on large text corpora via different learning (supervisor, unsupervised, semi-supervised, and self-supervised) techniques, to provide respective insights into these two types of tasks in NLP, precisely, Keyphrase Extraction (KPE) and Keyphrase Generation (KPG). We introduce appropriate taxonomies for PLM-KPE and KPG to highlight these two main tasks of NLP. Moreover, we point out some promising future directions for predicting keyphrases.