Abstract: Implementations generally relate to a method of matching job-seekers and employers in real-time with low required effort by the job-seekers and employers. A system-wide taxonomy is used in matching job-seekers and employers, thereby reducing the time for the recruiting process. Employers create a job position, and specify the relative importance of traits associated with the job position. Job-seekers answer profile questions (e.g. work experience), and the system uses the taxonomy to generate a list of job-seeker skills based on the answers to these questions. A list of job-seekers is then generated for the job position based on a mapping of the available job-seekers' skills and the employer rankings of the job position traits. A number of additional factors, like job seekers' in-app engagement, are used to further sort and filter the list of job-seekers.

Abstract: Implementations generally relate to a method of matching job-seekers and employers in real-time with low required effort by the job-seekers and employers. A system-wide taxonomy is used in matching job-seekers and employers, thereby reducing the time for the recruiting process. Employers create a job position, and specify the relative importance of traits associated with the job position. Job-seekers answer profile questions (e.g. work experience), and the system uses the taxonomy to generate a list of job-seeker skills based on the answers to these questions. A list of job-seekers is then generated for the job position based on a mapping of the available job-seekers' skills and the employer rankings of the job position traits. A number of additional factors, like job seekers' in-app engagement, are used to further sort and filter the list of job-seekers.

Abstract: Presented here are systems and methods to efficiently provide an answer to a query regarding an event and a third-party not expressly involved with the event. To efficiently provide the answer, mutually referential data structures can be created, each data structure representing a different aspect of the event. The mutually referential data structures can include references to each other. One or more of the mutually referential data structures can be recorded in a block chain ledger. For example, one data structure can represent a time ordered list of events, another data structure can represent the third-party, event categories, and the events associated with the third-party in the event categories, while another data structure can define the current state and represent most recent events containing an event category. But representing multiple aspects of the transaction, the efficiency of the lookup can be increased by up to eight orders of magnitude.