The paper aims to investigate social commerce systems from a systems thinking perspective. It proposes to model the social commerce process and outlines how Following, Communicating, Purchasing, and Sharing are systematically connected with each other in the social commerce process. The paper describes an exploratory review study using the systematic literature review method, including 384 social commerce research papers, which were published from 2011 to 2021. The data are refined by documentary analysis, including Study Selection Criteria and Quality Assessment processes. The paper systematically develops a conceptual framework for understanding social commerce. Previous research on social commerce mainly focuses on one or more particular key success factors (such as trust) in social commerce, and a few of them investigate social commerce as an integral business system. This review provides a more comprehensive basis for future social commerce research.

In this paper, we present a new technique, called stream projected ouliter detector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique in a number of aspects. First, SPOT employs a novel window-based time model and decaying cell summaries to capture statistics from the data stream. Second, sparse subspace template (SST), a set of top sparse subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective genetic algorithm (MOGA) is employed as an effective search method in unsupervised learning for finding outlying subspaces from training data. Finally, SST is able to carry out online self- evolution to cope with dynamics of data streams. This paper provides details on the motivation and technical challenges of detecting outliers from high-dimensional data streams, present an overview of SPOT, and give the plans for system demonstration of SPOT.