Add to ORKGApplications of aggregation for information summary have great meanings in various fields. In big data era, processing aggregate function in parallel is drawing researchers' attention. The aim of our work is to propose a generic framework enabling to map an arbitrary aggregation into a generic algorithm and identify when it can be efficiently executed on modern large-scale data-processing systems. We describe our preliminary results regarding classes of symmetric and asymmetric aggregation that can be mapped, in a systematic way, into efficient MapReduce-style algorithms
Aggregations help computing summaries of a data set, which are ubiquitous in various big data analyt...
Traditional databases are facing problems of scalability and efficiency dealing with a vast amount o...
In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented th...
Applications of aggregation for information summary have great meanings in various fields. In big da...
We study the problems of decomposing and sharing user-defined aggregate functions in distributed and...
Partial aggregation is of great importance in many dis-tributed data-parallel systems. Most notably,...
The emergence of the Internet as a computing platform increases the demand for new classes of algori...
The integration of computers into many facets of our lives has made the collection and storage of st...
Aggregation has been an important operation since the early days of relational databases. Today's Bi...
Due to their size and complexity, massive data sets bring many computational challenges for statisti...
Abstract. MapReduce, being inspired by the map and reduce primi-tives available in many functional l...
In this paper, we postulate computation as a key element in assuring the consistency of a family of ...
Computing multiple related group-bys and aggregates is one of the core operations of On-Line Analyti...
Aggregate operators are a useful class of operators in rela-tional databases. In this paper, we exam...
We examine the problem of eciently computing sum/count/avg aggregates over objects with nonzero ext...
Aggregations help computing summaries of a data set, which are ubiquitous in various big data analyt...
Traditional databases are facing problems of scalability and efficiency dealing with a vast amount o...
In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented th...
Applications of aggregation for information summary have great meanings in various fields. In big da...
We study the problems of decomposing and sharing user-defined aggregate functions in distributed and...
Partial aggregation is of great importance in many dis-tributed data-parallel systems. Most notably,...
The emergence of the Internet as a computing platform increases the demand for new classes of algori...
The integration of computers into many facets of our lives has made the collection and storage of st...
Aggregation has been an important operation since the early days of relational databases. Today's Bi...
Due to their size and complexity, massive data sets bring many computational challenges for statisti...
Abstract. MapReduce, being inspired by the map and reduce primi-tives available in many functional l...
In this paper, we postulate computation as a key element in assuring the consistency of a family of ...
Computing multiple related group-bys and aggregates is one of the core operations of On-Line Analyti...
Aggregate operators are a useful class of operators in rela-tional databases. In this paper, we exam...
We examine the problem of eciently computing sum/count/avg aggregates over objects with nonzero ext...
Aggregations help computing summaries of a data set, which are ubiquitous in various big data analyt...
Traditional databases are facing problems of scalability and efficiency dealing with a vast amount o...
In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented th...