While computing resources have continued to grow, methods for building and using large heuristics have not seen significant advances in recent years. We have observed that direction-optimizing breadth-first search, developed for and used broadly in the Graph 500 competition, can also be applied for building heuristics. But, the algorithm cannot run efficiently using external memory -- when the heuristics being built are larger than RAM. This paper shows how to modify direction-optimizing breadth-first search to build external-memory heuristics. We show that the new approach is not effective in state spaces with low asymptotic branching factors, but in other domains we are able to achieve up to a 3x reducing in runtime when building an external-memory heuristic. The approach is then used to build a 2.6TiB Rubik's Cube heuristic with 5.8 trillion entries, the largest pattern database heuristic ever built.

The minimum weighted vertex cover (MWVC) problem is a well known combinatorial optimization problem with important applications. This paper introduces a novel local search algorithm called NuMWVC for MWVC based on three ideas. First, four reduction rules are introduced during the initial construction phase. Second, the configuration checking with aspiration is proposed to reduce cycling problem. Moreover, a self-adaptive vertex removing strategy is proposed to save time.

Finding a right doctor with suitable expertise that meets one's health needs is important yet challenging. In this paper, we study the problem of finding high-rated doctors for a specific disease using imbalanced and heterogeneous healthcare experience rating data. We develop a data analytical framework, namely Dr. Right!, which incorporates the so-called network-textual embeddings, together with data-imbalance-aware mixture multi-classification models to rate doctors per specific disease. First, Dr. Right! collects the comments and rating records from patients for doctors on specific diseases from an online hospital and constructs a doctor-patient-disease network, where every edge weight is a pairwise average rating (experience score) among doctors, patients, and diseases. Then, Dr. Right! learns the embeddings of patient experiences from textual comments using the Word2Vec, as well as the embeddings of doctors and diseases from the doctor-patient-disease network via the Node2Vec. The two types of embeddings are fused to represent a doctor-patient pair. With the embedding representations of doctor-patient pairs, Dr. Right! learns an adaptively-weighted mixture multi-classification model to map a doctor-disease pair to an experience rating score, while addressing the challenges of data imbalance and group heterogeneity. Finally, extensive experimental results demonstrate the enhanced performances of Dr. Right! for predicting the disease-specific experience scores of doctors.