DreamCreek is a system for grading the capacity of lithium-ion battery cells. An electric vehicle (EV) battery pack consists of thousands of lithium-ion cells; these cells must have a balanced capacity, measured by a formation and grading phase. This phase is costly, taking 14-20+ hours. DreamCreek aims to optimize this process, by collecting data from partial charge. Using the data, it determines the capacity of lithium-ion cells by employing both machine learning prediction and logic deduction, to reduce the usage of energy and increase the production. We will demonstrate how DreamCreek works with a guided tour, and show how it reduces the time of the formation and grading phase to 4 hours, with an error rate in the range of [0.06 %, 1%].

This paper studies cost-effective graph cleaning with a single machine. We adopt a rule-based method that may embed machine learning models as predicates in the rules. Graph cleaning with the rules involves rule discovery, error detection and correction. These tasks are both computation-heavy and I/O-intensive as they repeatedly invoke costly graph pattern matching, and produce a large amount of a large volume of intermediate results, among other things. In light of these, no existing single-machine system is able to carry out these tasks even on not-too-large graphs, even using GPUs. Thus we develop MiniClean, a single-machine system for cleaning large graphs. It proposes (1) a workflow that better fits a single machine by pipelining CPU, GPU and I/O operations; (2) memory footprint reduction with bundled processing and data compression; and (3) a multi-mode parallel model for SIMD, pipelined and independent parallelism, and their scheduling to maximize CPU--GPU synergy. Using real-life graphs, we empirically verify that MiniClean outperforms the SOTA single-machine systems by at least 65.34× and multi-machine systems with 32 nodes by at least 8.09×.

We demonstrate MedHunter, a system for assisting the early stage of drug development. MedHunter builds a biomedical knowledge graph DDKG by integrating data from eleven biochemical libraries and data banks, and aligning entities from different data sources by means of heterogeneous entity resolution. It identifies drug-disease associations and protein-protein interactions in DDKG by employing graph association rules (GARs). GARs use graph patterns to extract relevant entities and embed ML models as predicates. MedHunter discovers GARs from DDKG and incrementally enriches DDKG with external data; it cleans DDKG with a special form of GARs. We demonstrate MedHunter for its (a) interfaces, (b) data enrichment/cleaning, and (c) applications in target identification, drug-drug interaction and protein-protein interaction.

This paper studies association rule discovery in a graphG1by referencing an external graphG2with overlapping information. The objective is to enrichG1with relevant properties and links fromG2. As a testbed, we consider Graph Association Rules (GARs). We propose a notion of graph joins to enrichG1by aligning entities acrossG1andG2. We also introduce a graph filtering method to support graph joins, by fetching only the data ofG2that pertains to the entities ofG1, to reduce noise and the size of the fused data. Based on these we develop a parallel algorithm to discover GARs acrossG1andG2. Moreover, we provide an incremental GAR discovery algorithm in response to updates toG1andG2. We show that both algorithms guarantee to reduce parallel runtime when given more processors. Better yet, the incremental algorithm is bounded relative to the batch one. Using real-life and synthetic data, we empirically verify that the methods improve the accuracy of association analyses by 30.4% on average, and scale well with large graphs.

This paper develops Planar (Plug and play PRAM), a single-machine system for graph analytics by reusing existing PRAM algorithms, without the need for designing new parallel algorithms. Planar supports both out-of-core and in-memory analytics. When a graph is too big to fit into the memory of a machine, Planar adapts PRAM to limited resources by extending a fixpoint model with multi-core parallelism, using disk as memory extension. For an in-memory task, it dedicates all available CPU cores to the task, and allows parallelly scalable PRAM algorithms to retain the property, i.e. , the more cores are available, the less runtime is taken. We develop a graph partitioning and work scheduling strategy to accommodate subgraph I/O, balance memory usage and reduce runtime, beyond traditional partitioners for multi-machine systems. Using real-life graphs, we empirically verify that Planar outperforms SOTA in-memory and out-of-core systems in efficiency and scalability.

We demonstrate PrismX (PRAM with SSDs as Memory eXtension), a single-machine system for graph analytics. PrismX allows users to make practical use of existing PRAM algorithms without any change. To cope with the limited DRAM capacity, it employs NVMe SSDs as memory extension. Leveraging graph preprocessing, PrismX implements a series of system optimization strategies, which automatically and transparently adapt to the runtime workload, no matter whether the computation is CPU-bound or I/O-bound. We demonstrate PrismX for its (1) ease of programming by reusing PRAM algorithms, (2) efficiency by comparing with the state-of-the-art graph systems, single-machine or multi-machine, in-memory or out-of-core; (3) parallel scalability of in-memory PRAM algorithms, reducing runtime when more CPU cores are available; and (4) applications in credit risk assessment.

This paper presents MiniGraph, an out-of-core system for querying big graphs with a single machine. As opposed to previous single-machine graph systems, MiniGraph proposes a pipelined architecture to overlap I/O and CPU operations, and improves multi-core parallelism. It also introduces a hybrid model to support both vertex-centric and graph-centric parallel computations, to simplify parallel graph programming, speed up beyond-neighborhood computations, and parallelize computations within each subgraph. The model induces a two-level parallel execution model to explore both inter-subgraph and intra-subgraph parallelism. Moreover, MiniGraph develops new optimization techniques under its architecture. Using real-life graphs of different types, we show that MiniGraph is up to 76.1x faster than prior out-of-core systems, and performs better than some multi-machine systems that use up to 12 machines.