Amit Somech | Homepage

Short Bio

In 2008 I obtained a B.Sc (double major in Computer Science and History of the Middle East) from Haifa University.

I joined the TAU DB group in 2013 and in late 2014 completed my M.Sc duties.

Currently, my main research focus is on interactive data analysis , aiming to develop a data mangement framework for simplyfing the analysis process and make it more accessible to the general public.

December: A Declarative Tool for Crowd Member Selection

Yael Amsterdamer, Tova Milo, Amit Somech, Brit Youngmann
Demo PVLDB 9(13), 1485-1488, 2016


Adequate crowd selection is an important factor in the success of crowdsourcing platforms, increasing the quality and relevance of crowd answers and their performance in different tasks. The optimal crowd selection can greatly vary depending on properties of the crowd and of the task. To this end, we present December, a declarative platform with novel capabilities for flexible crowd selection. December supports the personalized selection of crowd members via a dedicated query language Member-QL. This language enables specifying and combining common crowd selection criteria such as properties of a crowd member’s profile and history, similarity between profiles in specific aspects and relevance of the member to a given task. This holistic, customizable approach differs from previous work that has mostly focused on dedicated algorithms for crowd selection in specific settings. To allow efficient query execution, we implement novel algorithms in December based on our generic, semanticallyaware definitions of crowd member similarity and expertise. We demonstrate the effectiveness of December and MemberQL by using the VLDB community as crowd members and allowing conference participants to choose from among these members for different purposes and in different contexts.

REACT: Context-Sensitive Recommendations for Data Analysis

Tova Milo, Amit Somech
Demo SIGMOD, 2016


Data analysis may be a difficult task, especially for non-expert users, as it requires deep understanding of the investigated domain and the particular context. In this demo we present REACT, a system that hooks to the analysis UI and provides the users with personalized recommendations of analysis actions. By matching the current user session to previous sessions of analysts working with the same or other data sets, REACT is able to identify the potentially best next analysis actions in the given user context. Unlike previous work that mainly focused on individual components of the analysis work, REACT provides a holistic approach that captures a wider range of analysis action types by utilizing novel notions of similarity in terms of the individual actions, the analyzed data and the entire analysis workflow.

We demonstrate the functionality of REACT, as well as its effectiveness through a digital forensics scenario where users are challenged to detect cyber attacks in real life data achieved from honeypot servers.

Managing General and Individual Knowledge in Crowd Mining Applications

Yael Amsterdamer, Susan B. Davidson, Anna Kukliansky, Tova Milo, Slava Novgorodov, and Amit Somech.
Conference Paper CIDR 2015


Crowd mining frameworks combine general knowledge, which can refer to an ontology or information in a database, with individual knowledge obtained from the crowd, which captures habits and preferences. To account for such mixed knowledge, along with user interaction and optimization is- sues, such frameworks must employ a complex process of reasoning, automatic crowd task generation and result analysis. In this paper, we describe a generic architecture for crowd mining applications. This architecture allows us to examine and compare the components of existing crowdsourcing systems and point out extensions required by crowd mining. It also highlights new research challenges and potential reuse of existing techniques/components. We exemplify this for the OASSIS project and for other prominent crowdsourcing frameworks.

OASSIS: Query Driven Crowd Mining

Yael Amsterdamer, Susan B. Davidson, Tova Milo, Slava Novgorodov , Amit Somech,
Conference Paper SIGMOD 2014


Crowd data sourcing is increasingly used to gather information from the crowd and to obtain recommendations. In this paper, we explore a novel approach that broadens crowd data sourcing by enabling users to pose general questions, to mine the crowd for potentially relevant data, and to receive concise, relevant answers that represent frequent, significant data patterns. Our approach is based on (1) a simple generic model that captures both ontological knowledge as well as the individual history or habits of crowd members from which frequent patterns are mined; (2) a query language in which users can declaratively specify their information needs and the data patterns of interest; (3) an efficient query evaluation algorithm, which enables mining semantically concise answers while minimizing the number of questions posed to the crowd; and (4) an implementation of these ideas that mines the crowd through an interactive user interface. Experimental results with both real-life crowd and synthetic data demonstrate the feasibility and effectiveness of the approach.

Ontology Assisted Crowd Mining

Yael Amsterdamer, Susan B. Davidson, Tova Milo, Slava Novgorodov , Amit Somech,
Demo PVLDB 7(13): 1597-1600, 2014


We present OASSIS (for Ontology ASSISted crowd mining), a prototype system which allows users to declaratively specify their information needs, and mines the crowd for answers. The answers that the system computes are concise and relevant, and represent frequent, significant data patterns. The system is based on (1) a generic model that captures both ontological knowledge, as well as the individual knowledge of crowd members from which frequent patterns are mined; (2) a query language in which users can specify their information needs and types of data patterns they seek; and (3) an efficient query evaluation algorithm, for mining semantically concise answers while minimizing the number of questions posed to the crowd.

Currrent Teaching

  • Fall 2016/17

    Database Systems

    The purpose of this course is to provide an introduction to the design and use of database systems. We begin by covering the relational model and the SQL languag, then study methods for database design, covering the entity relationship model. Finally, we touch on some advanced topics in database systems. The recitation classes will cover practical topics in database programming.

  • Fall 2016/17

    Workshop in Data Science

    The workshop will focus on knowledge extraction from raw data, using statistical tools and machine learning algorithms. The students will be required to design and implement such systems and present their results in class.